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1
Altheyabi, Jasir Adel. "The Digital Forensic Tools Accuracy and Specifications." Academic Journal of Research and Scientific Publishing 3, no. 35 (2022): 58–65. http://dx.doi.org/10.52132/ajrsp.e.2022.35.3.
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The research aims to provide an overview of computer forensics, the history of computer forensics tools, and the accuracy and specifications of these tools. With the great and accelerating technological development, the reliance on the Internet has become greater and stronger than before. The world has become dependent on technology in all production and economic operations. And we talked in the second axis of the search for The Computer Forensic Legal Requirement, and Presentation of the tools used in the criminal investigation and an explanation of each tool. The digital forensic investigation tools that we will explain in this research are FTK. Forensic Toolkit, Prodiscovery, Autopsy, p2commander, OSForensics. We conclude that digital investigation tools have outstanding performance on different mediums. It has high accuracy and efficiency in digital investigation, and no single tool is superior to some other tools in all media. With more than one tool on a range of devices, it improves the investigation and testimony capabilities of examinees during exploration.
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Bharath Kumar, R., Abhishek Baplawat, P. Prabakaran, A. Phani Sridhar, B. Saraswathi, and Maderametla Roja Rani. "Artificial Intelligence in Forensic Sciences: Bridging Systematic Challenges with Next-Generation Applications." Journal of Neonatal Surgery 14, no. 5S (2025): 639–50. https://doi.org/10.52783/jns.v14.2105.
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Forensic science has come a long way in the boost afforded by Artificial Intelligence (AI) that enabled the strengthening of forensic investigations in terms of accuracy, efficiency and automation. This research studies AI driven methods in important forensic domains like fingerprint and facial identification, cyber forensics, forensic toxicology, forensic imaging, etc. Four machine learning algorithms were implemented over forensic data to analyze data comprising Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Random Forest and K-Nearest Neighbors (KNN). On test data, results of CNN achieved 96.8% accuracy on fingerprint recognition and SVM achieved 92.3% accuracy on facial identification. The Random Forest model achieved the accuracy of 89.5% in classifying the cyberforensic logs and 87.2% in forensic toxicology classification with the KNN model. A comparative analysis was made, in which it was observed that AI driven methods are more a faster, more precise and more automated than traditional forensic techniques. Also, blockchain integration offered security and integrity for the digital forensic evidence. However, the very fact of such advancements has introduced a number of ethical questions like bias, data privacy and forensic decision making. The focus of future research should be on obtaining transparent AI models, addressing issues of ethics, and integration of AI with new forensic technologies. This work shows that AI is well placed to solve systematic problems in forensic science, increasing accuracy and efficiency of investigations.
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Al-mugern, ٍRafef, Siti Hajar Othman, and Arafat Al-Dhaqm. "An Improved Machine Learning Method by applying Cloud Forensic Meta-Model to Enhance the Data Collection Process in Cloud Environments." Engineering, Technology & Applied Science Research 14, no. 1 (2024): 13017–25. http://dx.doi.org/10.48084/etasr.6609.
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Cloud computing has revolutionized the way businesses operate by offering accuracy in Normalized Mutual Information (NMI). However, with the growing adoption of cloud services, ensuring the accuracy and validation of common processes through machine learning and clustering of these common concepts as well as of the processes generated by cloud forensics experts’ data in cloud environments has become a paramount concern. The current paper proposes an innovative approach to enhance the data collection procedure in cloud environments by applying a Cloud Forensic Meta-Model (CFMM) and integrating it with machine learning techniques to improve the cloud forensic data. Through this approach, consistency and compatibility across different cloud environments in terms of accuracy are ensured. This research contributes to the ongoing efforts to validate the clustering process for data collection in cloud computing environments and advance the field of cloud forensics for standardizing the representation of cloud forensic data, certifying NMI and accuracy across different cloud environments.
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Malik, Dr Pankaj, Harshit Jain, Vartika Bhawsar, Neha Patidar, and Tanishka Soni. "Development of a Machine Learning Framework for Real-Time PMI (Post-Mortem Interval) Estimation in Field Forensics." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 09 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem37561.
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Accurate estimation of the Post-Mortem Interval (PMI) is critical in forensic investigations, aiding in determining the time of death. However, traditional PMI estimation methods, often reliant on physiological observations and environmental factors, face significant limitations in accuracy and efficiency, especially in field conditions. This paper presents the development of a machine learning (ML) framework designed for real-time PMI estimation, integrating multimodal sensor data to address the challenges encountered in field forensics. Our framework utilizes environmental and physiological features, including body temperature, ambient humidity, and biochemical decomposition markers, to predict PMI with high precision. The ML model, trained on historical forensic data, is deployed on a real-time processing platform, enabling rapid analysis and decision-making in resource- constrained environments. The system is optimized for field operations, incorporating low-power hardware and edge computing capabilities to provide forensic investigators with reliable PMI estimates on-site. Through a series of controlled experiments simulating forensic scenarios, our framework demonstrates a significant improvement in PMI accuracy compared to traditional methods, while maintaining low latency for real-time applications. This research highlights the potential of machine learning to revolutionize forensic practices, offering a scalable and adaptive solution for time-sensitive investigations. Here are some relevant keywords for the development of a machine learning framework for real-time PMI (Post- Mortem Interval) estimation in field forensics: Keywords: Field Forensics, Real-Time Machine Learning, Body Decomposition Stages, Machine Learning in Forensic Science, Artificial Intelligence for PMI Analysis, Sensor Data in PMI Estimation, Deep Learning for PMI Estimation, Automated Forensic Analysis, Data Acquisition in Field Forensics.
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Arthanari, Abirami, S. Shaan Raj, and Vignesh Ravindran. "A Narrative Review in Application of Artificial Intelligence in Forensic Science: Enhancing Accuracy in Crime Scene Analysis and Evidence Interpretation." Journal of International Oral Health 17, no. 1 (2025): 15–22. https://doi.org/10.4103/jioh.jioh_162_24.
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Abstract Aim: This review examines the transformative potential of artificial intelligence (AI) in forensic science, emphasizing its applications in crime scene analysis, evidence interpretation, digital forensics, and forensic odontology. It highlights AI’s ability to enhance accuracy, efficiency, and reliability while addressing ethical and practical challenges. Materials and Methods: A systematic search was conducted across PubMed, Web of Science, Scopus, and Google Scholar, complemented by manual reviews of key forensic journals and grey literature. The review included studies on AI applications in forensic odontology and other forensic domains published in the past decade. Predefined inclusion and exclusion criteria were applied, and duplicates were removed. Full-text reviews were conducted to ensure relevance, with disagreements resolved through consensus by a third reviewer to ensure rigor. Results: AI has significantly enhanced forensic practices by automating evidence analysis and improving accuracy. It streamlines crime scene reconstruction, accelerates digital forensic processes by analyzing large datasets, and advances dental forensics through rapid victim identification and bite mark analysis. AI-powered biometric systems enhance suspect and victim identification through facial recognition and pattern-matching technologies. However, limitations such as algorithmic bias, data privacy issues, and resource disparities pose challenges to its widespread adoption. Conclusion: AI is revolutionizing forensic science by providing enhanced precision, efficiency, and reliability in investigations. Addressing ethical concerns such as transparency, fairness, and algorithmic accountability is crucial for its responsible implementation. Future advancements should prioritize the development of explainable and unbiased algorithms, privacy-preserving techniques, and ethical frameworks. Interdisciplinary collaborations and global policy guidelines are essential to ensure the equitable and responsible integration of AI in forensic science, ultimately advancing justice and equity in the criminal justice system.
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Prambudi, Rizal Prambudi, Imam Riadi, and Murinto. "Analisis Forensik Mobile pada Aplikasi E-Commerce Menggunakan Metode Association of Chief Police Officers." Cyber Security dan Forensik Digital 8, no. 1 (2025): 44–52. https://doi.org/10.14421/csecurity.2025.8.1.5234.
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Media sosial merupakan platform daring yang memungkinkan komunikasi tanpa batas waktu dan lokasi, memfasilitasi interaksi antar pengguna. Walaupun fungsionalitasnya luas, media sosial sering disalahgunakan untuk berbagai tindakan kejahatan siber. Salah satu aplikasi yang banyak disalahgunakan untuk kegiatan ilegal adalah aplikasi e-commerce yang menyediakan berbagai transaksi jual beli. Aplikasi e-commerce menyimpan data pribadi di perangkat digital yang bisa digunakan sebagai bukti digital dalam kegiatan kriminal. Penelitian ini bertujuan mendapatkan bukti digital dari skenario kasus penipuan aplikasi e-commerce dengan menerapkan teknik forensik pada perangkat mobile serta menggunakan kerangka kerja Association of Chief Police Officers (ACPO). Bukti digital meliputi gambar, video, dan pesan teks. Keakuratan tools yang digunakan dengan mengintegrasikan metode ACPO dalam mengekstraksi bukti digital juga menjadi fokus dalam penelitian. Penelitian ini menggunakan Oxygen Forensic Detective dan MOBILedit Forensic Express sebagai tools. Bukti digital yang berhasil dipulihkan dari perangkat bukti berupa gambar dan video. Hasil penelitian menunjukkan bahwa Oxygen Forensic Detective berhasil memperoleh bukti digital sebesar 37%, sementara MOBILedit Forensic Express memperoleh bukti sebesar 26%. Hasil ini mengindikasikan bahwa metode forensik digital efektif dalam mengungkap bukti kejahatan di aplikasi e-commerce, dengan Oxygen Forensic Detective lebih unggul dibandingkan MOBILedit Forensic Express. Pemilihan alat forensik yang tepat berperan penting dalam investigasi digital.Kajian ini diharapkan dapat mendorong penerapan integrasi metode ACPO dan penggunaan tools forensik dalam mengungkapkan bukti digital kejahatan melalui e-commerce. Kata kunci: media sosial, forensik digital, kejahatan siber, ACPO, aplikasi e-commerce -------------------------- Abstract Social media is an online platform that enables communication without time and location constraints, facilitating interaction between users. However, these platforms are often misused for various cybercrimes. One type of application frequently exploited for illegal activities is e-commerce applications, which provide various buying and selling transactions. E-commerce applications store personal data on digital devices that can be used as digital evidence in criminal activities. This study aims to obtain digital evidence from a fraud case scenario involving an e-commerce application by applying forensic techniques on mobile devices and using the Association of Chief Police Officers (ACPO) framework. The digital evidence includes images, videos, and text messages. Another objective of this research is to assess the accuracy of the forensic tools used when integrating the ACPO method to extract digital evidence. This study utilizes Oxygen Forensic Detective and MOBILedit Forensic Express as the forensic tools. The digital evidence successfully retrieved from the device consists of images and videos. The results show that Oxygen Forensic Detective managed to extract 37% of digital evidence, while MOBILedit Forensic Express recovered 26%. In conclusion, digital forensic techniques are effective in uncovering evidence of crimes within e-commerce applications, with Oxygen Forensic Detective outperforming MOBILedit Forensic Express. Selecting the appropriate forensic tool plays a crucial role in digital investigations. This research is expected to encourage the integration of the ACPO method and the use of forensic tools in revealing digital evidence of crimes conducted through e-commerce. Keywords: social media, digital forensics, cybercrime, ACPO, e-commerce applications
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Alex, Makheti, Gilbert Gilibrays Ocen, Badru Lusiba, et al. "A Process Model to Enhance the Accuracy of Digital Forensic Investigations: A case of National Identification and Registration Authority (NIRA-Uganda)." East African Journal of Information Technology 5, no. 1 (2022): 216–43. http://dx.doi.org/10.37284/eajit.5.1.1015.
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The field of digital forensics has become commonplace due to the increasing prevalence of technology since the late 20th century and the inevitable relevance of this technology in the conducting of criminal activity. In traditional forensics, the evidence is generally something tangible that could identify the criminal, such as hair, blood or fingerprints. In contrast, digital forensics deals with files and data in digital form extracted from digital devices like computers, and phones, among other digital devices, meaning is derived from the fact that a computer or computerised device is the subject or object of crime. In this paper, we explore the challenges faced by the National Identification and Registration Authority (NIRA) digital forensic investigation and develop a process model that enhances the accuracy of digital forensic investigation. We adopted a mixed method approach of research involving qualitative, quantitative and experimental design. The study makes significant findings in areas of enhancement accuracy of digital forensic investigation by enumerating the processes that must be followed. As a recommendation for future work, for purposes of generalisation of the study findings, a wider study involving other security agencies such as the police should be conducted.
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Sutikno, Tole. "Mobile forensics tools and techniques for digital crime investigation: a comprehensive review." International Journal of Informatics and Communication Technology (IJ-ICT) 13, no. 2 (2024): 321. http://dx.doi.org/10.11591/ijict.v13i2.pp321-332.
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<p>Extracting and analyzing data from smartphones, IoT devices, and drones is crucial for conducting digital crime investigations. Effective cyberattack mitigation necessitates the use of advanced Android mobile forensics techniques. The investigation necessitates proficiency in manual, logical, hex dump, chip-off, and microread methodologies. This paper provides a comprehensive overview of Android mobile forensics tools and techniques for digital crime investigation, as well as their use in gathering and analyzing evidence. Forensic software tools like Cellebrite UFED, Oxygen Forensic Detective, XRY by MSAB, Magnet AXIOM, SPF Pro by SalvationDATA, MOBILedit Forensic Express, and EnCase Forensic employ both physical and logical techniques to retrieve data from mobile devices. These advanced tools offer a structured approach to tackling digital crimes effectively. We compare dependability, speed, compatibility, data recovery accuracy, and reporting. Mobile-network forensics ensures data acquisition, decryption, and analysis success. Conclusions show that Android mobile forensics tools for digital crime investigations are diverse and have different capabilities. Mobile forensics software offers complete solutions, but new data storage and encryption methods require constant development. The continuous evolution of forensic software tools and a comprehensive tool classification system could further enhance digital crime investigation capabilities.</p>
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Tole, Sutikno. "Mobile forensics tools and techniques for digital crime investigation: a comprehensive review." International Journal of Informatics and Communication Technology 13, no. 2 (2024): 321–32. https://doi.org/10.11591/ijict.v13i2.pp321-332.
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Extracting and analyzing data from smartphones, IoT devices, and drones is crucial for conducting digital crime investigations. Effective cyberattack mitigation necessitates the use of advanced Android mobile forensics techniques. The investigation necessitates proficiency in manual, logical, hex dump, chip-off, and microread methodologies. This paper provides a comprehensive overview of Android mobile forensics tools and techniques for digital crime investigation, as well as their use in gathering and analyzing evidence. Forensic software tools like Cellebrite UFED, Oxygen Forensic Detective, XRY by MSAB, Magnet AXIOM, SPF Pro by SalvationDATA, MOBILedit Forensic Express, and EnCase Forensic employ both physical and logical techniques to retrieve data from mobile devices. These advanced tools offer a structured approach to tackling digital crimes effectively. We compare dependability, speed, compatibility, data recovery accuracy, and reporting. Mobile-network forensics ensures data acquisition, decryption, and analysis success. Conclusions show that Android mobile forensics tools for digital crime investigations are diverse and have different capabilities. Mobile forensics software offers complete solutions, but new data storage and encryption methods require constant development. The continuous evolution of forensic software tools and a comprehensive tool classification system could further enhance digital crime investigation capabilities.
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Mahar, Mashooque Ali, Asad Raza, Raja Sohail Ahmed Larik, Asadullah Burdi, Muhammad Shabbir, and Mudassir Iftikhar. "Transformative Role of LLMs in Digital Forensic Investigation: Exploring Tools, Challenges, and Emerging Opportunities." VAWKUM Transactions on Computer Sciences 13, no. 1 (2025): 217–29. https://doi.org/10.21015/vtcs.v13i1.2127.
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In the evolving realm of digital forensics, the admissible nature of trustworthy digital evidence in a court of law necessitates the application of scientifically validated digital forensic investigative techniques to substantiate a suspected security event. The incorporation of LLMs represents a transformative technology, set to enhance the efficiency and accuracy of digital forensics investigations. A thorough literature analysis is conducted, including current digital forensic models, tools, large language models (LLMs), deep learning methodologies, and the application of LLMs in investigative processes. The review delineates the issues in current digital forensic methodologies and examines the barriers and potential of integrating LLMs. This study emphasizes the need of integrating LLMs into digital forensics, providing insights into their advantages, disadvantages, and wider implications for addressing contemporary cyber threats.
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Yuwono, Doddy Teguh, Abdul Fadlil, and Sunardi Sunardi. "Performance Comparison of Forensic Software for Carving Files using NIST Method." Jurnal Teknologi dan Sistem Komputer 7, no. 3 (2019): 89–92. http://dx.doi.org/10.14710/jtsiskom.7.3.2019.89-92.
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Data lost due to the fast format or system crash will remain in the media sector of storage. Digital forensics needs proof and techniques for retrieving data lost in storage. This research studied the performance comparison of open-source forensic software for data retrieval, namely Scalpel, Foremost, and Autopsy, using the National Institute of Standards Technology (NIST) forensic method. The testing process was carried out using the file carving technique. The carving file results are analyzed based on the success rate (accuracy) of the forensic tools used in returning the data. Scalpel performed the highest accuracy for file carving of 100% success rate for 20 document files in pdf and Docx format, and 90% for 10 image files in png and jpeg format.
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Lim, Michelle Chee Ern, Brandon Hong Chow Chen, Le Ying Lim, et al. "Analysis of Forensic Disk Imaging Tools for Data Acquisition and Preservation." Journal of Informatics and Web Engineering 4, no. 2 (2025): 158–81. https://doi.org/10.33093/jiwe.2025.4.2.11.
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The identification, preservation, analysis, and presentation of electronic evidence to support legal or organizational inquiries constitute the discipline of digital forensics, which is crucial to contemporary investigations. A crucial component of forensic inquiry, disk imaging guarantees precision, dependability, and legal defensibility. To preserve the original evidence, disk imaging makes an identical, bit-by-bit duplicate of a digital storage device, capturing hidden data, deleted material, and active files. Given the critical role of disk imaging in forensic investigations, selecting the right tool is crucial for accuracy, efficiency, and compliance with forensic standards. This study assesses widely used tools, including AccessData FTK Imager, Guymager, X-Ways Forensics, OSForensics, and FTK Imager, to help researchers and industry professionals choose the most suitable option for their investigative needs. This research examines the usability, imaging speed, supported hashing techniques, supported output formats, and other aspects of each tool to assess their suitability for usage in various forensic scenarios. The shows that X-Ways Forensic is among the greatest imaging tools because of its wide range of supported operations, fast imaging speed, and format compatibility. The result of hash verification, perfectly matched with source data, again establishes the capability of AccessData FTK Imager, FTK Imager, Guymager, X-Ways Forensics, and OS Forensics to ensure forensic soundness. Its capability to generate a detailed report with comprehensive drive geometry and file segmentation establishes its applicability in forensic workflows. Besides, the time consumed for processing shows its applicability in time-critical investigations too.
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Reddi, Nallamilli V. K., Karangi Sindhu, Shaik Ahmed Aaquelah, Mohammad Azarunnisa, and Palleti Nikhil. "DEEP-TRUST: DEEPFAKE DETECTION VIA HYBRID CNN-ELA-GAN." International Journal of Engineering Applied Sciences and Technology 09, no. 12 (2025): 88–91. https://doi.org/10.33564/ijeast.2025.v09i12.011.
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—Deepfakes pose a growing threat to digital security and trust, demanding robust methods to detect AI-generated manipulations. Traditional approaches like XceptionNet and Error Level Analysis (ELA), while foundational, struggle with evolving generative architectures like diffusion models and fail to balance accuracy with interpretability. Static forensic methods also lack adaptability to dynamic adversarial attacks. This study introduces Deep-Trust, a hybrid framework integrating Convolutional Neural Networks (CNNs), Error Level Analysis (ELA), and Generative Adversarial Networks (GANs) to expose and localize deepfake artifacts. We analyzed 5,000+ images from benchmark datasets (Face Forensics++, Celeb-DF) and identified 12 critical forensic features, including compression anomalies, texture inconsistencies, and spectral distortions. A twostage preprocessing pipeline was designed: first, ELA amplifies pixel-level compression artifacts by recompressing images at varying JPEG quality levels, and second, a GAN-based adversarial training module generates synthetic deepfakes to harden the detector against unseen manipulations. Unlike conventional models, the CNN-ELA-GAN framework dynamically optimizes feature weights and hyperparameters through adversarial training, enhancing both detection accuracy and computational efficiency. The dual-branch CNN processes ELA maps and raw images in parallel, fusing low-level forensic cues with high-level semantic features. Gradient-weighted Class Activation Mapping (Grad-CAM) further localizes tampered regions (e.g., distorted eyes or synthetic hair textures) with humaninterpretable heatmaps. Evaluated on 10,000+ samples, Deep-Trust achieved 98.7% accuracy and 28 FPS inference speed on NVIDIA A100 GPUs, outperforming XceptionNet (94.2% accuracy) and ELA-only baselines (82.1% accuracy). The optimized 12-feature configuration reduced training time by 33% (from 120s to 80s per epoch) while maintaining robustness against adversarial attacks. This work demonstrates the synergy of forensic analysis, deep learning, and adversarial training for combatting deepfakes, offering a scalable solution for real-time social media moderation, digital forensics, and secure authentication systems.
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Anushka Deepak Kadage. "AI-Enhanced Digital Forensics: Automated Techniques for Efficient Investigation and Evidence Collection." Journal of Electrical Systems 20, no. 1s (2024): 211–29. http://dx.doi.org/10.52783/jes.766.
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The abstract summarizes AI-enhanced digital forensics topics. It highlights the importance of AI in digital forensic investigations and outlines its major features, historical perspectives, and methodological evolution. The abstract describes how automated methods can streamline evidence collection and investigation. The historical perspective highlights digital forensic procedures from rudimentary file system investigations to AI-driven methods. This progression reflects digital crime's dynamic character and forensic method developments. The AI-enhanced digital forensics methodology includes establishing an effective component model, identifying datasets, gathering data, arranging studies, and considering ethical considerations. Representative datasets and ethical considerations are stressed in the abstract to ensure ethical and responsible AI application in forensic investigations. AI-based systems are evaluated using accuracy, false positive/negative rates, speed and efficiency, scalability, and durability. A straightforward comparison of these parameters across AI algorithms using bar graphs and grouped bar charts helps forensic investigators chooses strategies. In conclusion, AI-enhanced digital forensics is well understood, and performance evaluations, methodological concerns, historical evolution, and ethics are important. AI is being used in digital forensics as technology advances, giving investigators a strong tool to navigate the digital world accurately and efficiently. To use AI responsibly and effectively for justice, technique and ethics must be constantly improved
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Sharma, Puneet, Deepak Arora, and T. Sakthivel. "Mobile Cloud Forensic Readiness Process Model for Cloud-Based Mobile Applications." International Journal of Digital Crime and Forensics 12, no. 3 (2020): 58–76. http://dx.doi.org/10.4018/ijdcf.2020070105.
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Nowadays, mobile cloud applications have attracted millions of smartphone users due to the proliferation of cyber technologies and a wide range of applications. Mobile cloud forensic investigation methodologies need tremendous growth due to the increasing crime incidents. The forensic readiness model plays a significant role in the forensic investigation framework by ensuring the ease of investigation to the forensic investigator. The existing forensic readiness supports either the mobile device or cloud environment, which lacks to prepare the information for the investigation. This article presents a mobile cloud forensic readiness process model to identify the factors and prepare the information that effectively supports forensic investigations. The proposed model involves requirements for the mobile cloud forensics from multiple perspectives with the aim of developing the forensic-ready system. As a result, the proposed forensic readiness model enables the mobile cloud forensic to improve the accuracy of investigation as well as reduces the investigation time significantly.
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Sunny, Antil, and Joon Vandana. "Biotechnology in Forensic Science: Advancements and Applications." Journal of Forensic Science and Research 7, no. 1 (2025): 007–14. https://doi.org/10.29328/journal.jfsr.1001073.
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Background: Biotechnology is a multidisciplinary field based on the expertise of molecular biology, chemistry, biochemistry, chemical and biological engineering, and digital computing. Biotechnology plays an important role in modern forensic science, driving advances in analytical tools and techniques. This review study provides a brief overview of applications, highlighting advances in forensic biotechnology and key technologies involved in the domains of genomics and DNA analysis, microbial forensics, forensic medicine, and forensic serology. The integration of forensic expertise with technology has increased the accuracy, sensitivity, and efficiency of forensic casework. Conclusions: This interdisciplinary field extends beyond its usual association with biology to also include chemistry, fingerprint analysis, and toxicology, among others. Continued progress and innovation in this advanced field will further enhance investigative capabilities and facilitate the pursuit of justice.
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Yudhana, Anton, Imam Riadi, and Riski Yudhi Prasongko. "Forensik WhatsApp Menggunakan Metode Digital Forensic Research Workshop (DFRWS)." Jurnal Informatika: Jurnal Pengembangan IT 7, no. 1 (2022): 43–48. http://dx.doi.org/10.30591/jpit.v7i1.3639.
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Smartphone technology makes cybercrime crimes increase from year to year, one of the smartphone applications used to commit crimes is WhatsApp. The WhatsApp application is one of the most widely used social media, especially in Indonesia. Criminal acts such as hate speech, fraud, and defamation often occur on WhatsApp social media. This research was conducted to find forensic evidence on the WhatsApp social media application using the Digital Forensics Research Workshop (DFRWS) method. The stages of digital forensics include identification, preservation, collection, examination, analysis and presentation in finding digital evidence of cybercrime using the MOBILedit Forensic Express and HashMyFiles software applications. The digital evidence sought on smartphones can be found using case scenarios with 13 parameters that have been created. The results of this study indicate that the digital forensic software MOBILedit Forensic Express can detect types of digital evidence with an accuracy rate of 84.6%, while Hashmyfiles can detect the authenticity of digital evidence by 100%.
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Morić, Zlatan, Vedran Dakić, Ana Kapulica, and Damir Regvart. "Forensic Investigation Capabilities of Microsoft Azure: A Comprehensive Analysis and Its Significance in Advancing Cloud Cyber Forensics." Electronics 13, no. 22 (2024): 4546. http://dx.doi.org/10.3390/electronics13224546.
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This article delves into Microsoft Azure’s cyber forensic capabilities, focusing on the unique challenges in cloud security incident investigation. Cloud services are growing in popularity, and Azure’s shared responsibility model, multi-tenant nature, and dynamically scalable resources offer unique advantages and complexities for digital forensics. These factors complicate forensic evidence collection, preservation, and analysis. Data collection, logging, and virtual machine analysis are covered, considering physical infrastructure restrictions and cloud data transience. It evaluates Azure-native and third-party forensic tools and recommends methods that ensure effective investigations while adhering to legal and regulatory standards. It also describes how AI and machine learning automate data analysis in forensic investigations, improving speed and accuracy. This integration advances cyber forensic methods and sets new standards for future innovations. Unified Audit Logs (UALs) in Azure are examined, focusing on how Azure Data Explorer and Kusto Query Language (KQL) can effectively parse and query large datasets and unstructured data to detect sophisticated cyber threats. The findings provide a framework for other organizations to improve forensic analysis, advancing cloud cyber forensics while bridging theoretical practices and practical applications, enhancing organizations’ ability to combat increasingly sophisticated cybercrime.
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Qamhan, Mustafa, Yousef A. Alotaibi, and Sid-Ahmed Selouani. "Source Microphone Identification Using Swin Transformer." Applied Sciences 13, no. 12 (2023): 7112. http://dx.doi.org/10.3390/app13127112.
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Microphone identification is a crucial challenge in the field of digital audio forensics. The ability to accurately identify the type of microphone used to record a piece of audio can provide important information for forensic analysis and crime investigations. In recent years, transformer-based deep-learning models have been shown to be effective in many different tasks. This paper proposes a system based on a transformer for microphone identification based on recorded audio. Two types of experiments were conducted: one to identify the model of the microphones and another in which identical microphones were identified within the same model. Furthermore, extensive experiments were performed to study the effects of different input types and sub-band frequencies on system accuracy. The proposed system is evaluated on the Audio Forensic Dataset for Digital Multimedia Forensics (AF-DB). The experimental results demonstrate that our model achieves state-of-the-art accuracy for inter-model and intra-model microphone classification with 5-fold cross-validation.
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Kenneth Chukwujekwu Nwafor, Ivan Zziwa, Daniel O. T. Ihenacho, and Oladele J Adeyeye. "Innovative approaches in complex data forensics: error rate assessment and its impact on cybersecurity." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 1772–92. http://dx.doi.org/10.30574/wjarr.2024.24.1.3212.
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In the realm of cybersecurity, the integrity of forensic investigations is paramount, especially as the volume and complexity of data continue to escalate. This paper explores innovative approaches to complex data forensics, focusing on the methodologies used to assess error rates in data retrieval and analysis. High error rates in forensic processes can compromise the reliability of findings, leading to erroneous conclusions that may impact security measures and legal proceedings. This research examines various techniques for error rate assessment, including statistical methods and data validation protocols, which serve to quantify the accuracy of forensic analysis. Furthermore, the paper discusses the profound implications that high error rates can have on the integrity of forensic findings, emphasizing the need for meticulous attention to detail in data handling and processing. To counter these challenges, we present strategies aimed at enhancing data reliability, such as implementing rigorous quality assurance processes, leveraging machine learning algorithms for anomaly detection, and utilizing advanced encryption methods to protect data integrity throughout the forensic lifecycle. By addressing the critical role of error rate assessment in data forensics, this research contributes to the broader discourse on cybersecurity and underscores the necessity of adopting robust methodologies to ensure accurate and reliable forensic outcomes in an increasingly complex digital landscape.
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Kenneth, Chukwujekwu Nwafor, Zziwa Ivan, O. T. Ihenacho Daniel, and J. Adeyeye Oladele. "Innovative approaches in complex data forensics: error rate assessment and its impact on cybersecurity." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 1772–92. https://doi.org/10.5281/zenodo.15045230.
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In the realm of cybersecurity, the integrity of forensic investigations is paramount, especially as the volume and complexity of data continue to escalate. This paper explores innovative approaches to complex data forensics, focusing on the methodologies used to assess error rates in data retrieval and analysis. High error rates in forensic processes can compromise the reliability of findings, leading to erroneous conclusions that may impact security measures and legal proceedings. This research examines various techniques for error rate assessment, including statistical methods and data validation protocols, which serve to quantify the accuracy of forensic analysis. Furthermore, the paper discusses the profound implications that high error rates can have on the integrity of forensic findings, emphasizing the need for meticulous attention to detail in data handling and processing. To counter these challenges, we present strategies aimed at enhancing data reliability, such as implementing rigorous quality assurance processes, leveraging machine learning algorithms for anomaly detection, and utilizing advanced encryption methods to protect data integrity throughout the forensic lifecycle. By addressing the critical role of error rate assessment in data forensics, this research contributes to the broader discourse on cybersecurity and underscores the necessity of adopting robust methodologies to ensure accurate and reliable forensic outcomes in an increasingly complex digital landscape.
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Wang, Yan, Qindong Sun, Dongzhu Rong, Shancang Li, and Li Da Xu. "Image Source Identification Using Convolutional Neural Networks in IoT Environment." Wireless Communications and Mobile Computing 2021 (September 10, 2021): 1–12. http://dx.doi.org/10.1155/2021/5804665.
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Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results show that the proposed method can effectively identify the source devices with high accuracy.
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Lai, Zhimao, Zhuangxi Yao, Guanyu Lai, Chuntao Wang, and Renhai Feng. "A Novel Face Swapping Detection Scheme Using the Pseudo Zernike Transform Based Robust Watermarking." Electronics 13, no. 24 (2024): 4955. https://doi.org/10.3390/electronics13244955.
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The rapid advancement of Artificial Intelligence Generated Content (AIGC) has significantly accelerated the evolution of Deepfake technology, thereby introducing escalating social risks due to its potential misuse. In response to these adverse effects, researchers have developed defensive measures, including passive detection and proactive forensics. Although passive detection has achieved some success in identifying Deepfakes, it encounters challenges such as poor generalization and decreased accuracy, particularly when confronted with anti-forensic techniques and adversarial noise. As a result, proactive forensics, which offers a more resilient defense mechanism, has garnered considerable scholarly interest. However, existing proactive forensic methodologies often fall short in terms of visual quality, detection accuracy, and robustness. To address these deficiencies, we propose a novel proactive forensic approach that utilizes pseudo-Zernike moment robust watermarking. This method is specifically designed to enhance the detection and analysis of face swapping by transforming facial data into a binary bit stream and embedding this information within the non-facial regions of video frames. Our approach facilitates the detection of Deepfakes while preserving the visual integrity of the video content. Comprehensive experimental evaluations have demonstrated the robustness of this method against standard signal processing operations and its superior performance in detecting Deepfake manipulations.
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Abdul Islam, Abdul Kalam, Y Veeranjaneyulu, G N V Bhavana, and S. Sriramana Swamy. "FOREINSICS IMAGE DETECTION AND CLASSIFICATION USING YOLO." Journal of Nonlinear Analysis and Optimization 15, no. 02 (2024): 889–93. https://doi.org/10.36893/jnao.2024.v15i2.139.
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In the realm of digital forensics, the accurate and efficient analysis of images is crucialfor the investigation and solving of crimes. This project presents a forensic image classification and detection methodology using the "You Only Look Once" (YOLO) algorithm, a state-of-the-art object detection model. YOLO's real-time detection capabilities and high accuracy make it a potent tool for forensic applications, where timely and precise identification of elements within an image can be critical.The research involves training the YOLO model on a comprehensive dataset of forensic images, encompassing various scenarios such as crime scenes, digital evidence, and suspicious activities. By leveraging YOLO's deep learning architecture, the model can accurately classify and detect objects and scenes.
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Tri Meilana, Andi Nur Sakina, Elza Ibrahim Auerkari, and Antonius Winoto Suhartono. "Perkembangan Teknologi Digital dalam Kedokteran Gigi Forensik." Indonesian Journal of Legal and Forensic Sciences (IJLFS) 14, no. 2 (2025): 95. https://doi.org/10.24843/ijlfs.2024.v14.i02.p03.
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In this modern era, technology continues to grow rapidly and has become part of our everyday life. Recent advances have popularized various digital technologies for their use in forensic dentistry, especially to assist individuals. Various digital recovery methods have been included including replacing conventional forensic methods in terms of acquisition, analysis, efficiency, and accuracy. This review aims to discuss the development of digital technology within the scope of forensic dentistry as well as various methods through digital technology that can be used in solving various forensic cases. The writing of this review was carried out through a literature review on various databases in the form of PubMed, SciELO, MEDLINE, and Scholar within the last 10 years. Various digital developments in forensic dentistry that have been published include radiography and digital photography, reconstructing fragments with 3D Printing, Intra Oral 3D Scanners, UV photography in aid of dental materials, superimposed methods, assisted digital prostheses, virdentopsy & teledentistry, as well as various device developments software and database. Of these various methods, almost all methods produce good accuracy, simpler methods without any destructive action on forensic evidence, and save time compared to conventional methods. However, no method has been universally applied worldwide. This is due to limitations in accessing tools and costs that are not relatively cheap. In the future, digital technology will play a crucial role in forensics especially if it can be accessed easily.
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Thomas, Richard M., Connie L. Parks, and Adam H. Richard. "Accuracy Rates of Ancestry Estimation by Forensic Anthropologists Using Identified Forensic Cases." Journal of Forensic Sciences 62, no. 4 (2017): 971–74. http://dx.doi.org/10.1111/1556-4029.13361.
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Jumagaliyeva, Ainur, Gulzhan Muratova, Lazzat Abdykerimova, Elmira Abdykerimova, and Aktolkyn Sagynbay. "EFFICIENT DIGITAL EVIDENCE COLLECTION METHOD USING VELOCIRAPTOR PROGRAM IN NETWORK SECURITY." Вестник КазАТК 134, no. 5 (2024): 224–38. https://doi.org/10.52167/1609-1817-2024-134-5-224-238.
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As cyberattacks grow in frequency and sophistication, digital forensics has become essential for enhancing network security and facilitating timely incident response. This article provides a comprehensive evaluation and demonstration of the practical use of Velociraptor, an open-source digital forensics and incident response tool, for efficient evidence collection and analysis in large-scale network environments. The study highlights how Velociraptor, through its customizable Velociraptor Query Language (VQL), significantly improves the speed, accuracy, and scalability of forensic investigations. A comparative analysis with established forensic tools underscores Velociraptor’s advantages in automating real-time data collection, flexible configuration, and large-scale deployment. In addition to discussing challenges such as the tool’s steep learning curve and resource demands, the article explores opportunities for further enhancement through artificial intelligence integration and performance optimization. The findings suggest that Velociraptor is a powerful asset for modernizing digital forensics and strengthening network security strategies.
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VENKATARAMANAN, B. MENAKA, and Arulselvarani S. "EXPLORING CLOUD NETWORK FORENSICS: A COMPREHENSIVE SURVEY OF TOOLS AND TECHNIQUES ENHANCED WITH DEEP LEARNING MODELS." International Journal of Information Technology, Research and Applications 4, no. 2 (2025): 14–28. https://doi.org/10.59461/ijitra.v4i2.174.
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A comprehensive investigation into cloud network forensics, providing an in-depth survey of tools and techniques while integrating advanced deep learning models for enhanced analysis. By examining various cloud environments, including public, private, and hybrid infrastructures, the study explores the challenges and opportunities in detecting, analyzing, and mitigating security incidents. This survey provides an overview of forensic tools and techniques used to investigate cyber incidents, focusing on the integration of deep learning models for enhanced threat detection and analysis. Through the utilization of deep learning methodologies, the research aims to enhance the efficiency and accuracy of forensic investigations, offering valuable insights into emerging trends and best practices for securing cloud-based systems. It highlights key challenges, including data privacy and real-time threat detection, while discussing future trends in AI-driven forensics.
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Onome, Dr Oghene Augustine. "Computer Forensics and Advanced Methodology." International Journal of Emerging Science and Engineering 11, no. 7 (2023): 1–15. http://dx.doi.org/10.35940/ijese.g2552.0611723.
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The field of computer forensics emerged in response to the substantial increase in computer-related crimes occurring annually. This rise in criminal activity can be attributed to the rapid expansion of the internet, which has provided perpetrators with increased opportunities for illicit actions. When a computer system is compromised and an intrusion is detected, it becomes crucial for a specialized forensics team to investigate the incident with the objective of identifying and tracing the responsible party. The outcome of such forensic efforts often leads to legal action being taken against those accountable for the wrongdoing. The methodology employed in computer forensics continually evolves alongside advancements in crime approaches, particularly as attackers leverage emerging technologies. To ensure the accuracy of forensic investigations, it is imperative that the scientific knowledge underlying the forensic process be complemented by the integration of technological tools. A plethora of hardware and software options are available to facilitate the analysis and interpretation of forensic data, thereby enhancing the efficiency and effectiveness of investigations. While the fundamental objectives of computer forensics primarily involve the seamless preservation, identification, extraction, documentation, and analysis of data, the widespread adoption of this discipline is contingent upon the law enforcement community's ability to keep pace with advancements in computing technology. Furthermore, the prevalence of diverse computer devices resulting from the emergence of microcomputer technology also plays a crucial role in shaping the field of computer forensics. This research paper aims to provide a comprehensive overview of computer forensics, encompassing advanced methodologies and detailing various technology tools that facilitate the forensic process. Specific areas of focus include the analysis of encrypted drives, disk analysis techniques, analysis toolkits, investigations involving volatile memory, and the examination of captured network packets. By exploring these aspects, this paper aims to contribute to the existing body of knowledge in the field of computer forensics and support practitioners in their pursuit of effective investigative techniques.
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Dr., Oghene Augustine Onome. "Computer Forensics and Advanced Methodology." International Journal of Emerging Science and Engineering (IJESE) 11, no. 7 (2023): 1–15. https://doi.org/10.35940/ijese.G2552.0611723.
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<strong>Abstract: </strong>The field of computer forensics emerged in response to the substantial increase in computer-related crimes occurring annually. This rise in criminal activity can be attributed to the rapid expansion of the internet, which has provided perpetrators with increased opportunities for illicit actions. When a computer system is compromised and an intrusion is detected, it becomes crucial for a specialized forensics team to investigate the incident with the objective of identifying and tracing the responsible party. The outcome of such forensic efforts often leads to legal action being taken against those accountable for the wrongdoing. The methodology employed in computer forensics continually evolves alongside advancements in crime approaches, particularly as attackers leverage emerging technologies. To ensure the accuracy of forensic investigations, it is imperative that the scientific knowledge underlying the forensic process be complemented by the integration of technological tools. A plethora of hardware and software options are available to facilitate the analysis and interpretation of forensic data, thereby enhancing the efficiency and effectiveness of investigations. While the fundamental objectives of computer forensics primarily involve the seamless preservation, identification, extraction, documentation, and analysis of data, the widespread adoption of this discipline is contingent upon the law enforcement community's ability to keep pace with advancements in computing technology. Furthermore, the prevalence of diverse computer devices resulting from the emergence of microcomputer technology also plays a crucial role in shaping the field of computer forensics. This research paper aims to provide a comprehensive overview of computer forensics, encompassing advanced methodologies and detailing various technology tools that facilitate the forensic process. Specific areas of focus include the analysis of encrypted drives, disk analysis techniques, analysis toolkits, investigations involving volatile memory, and the examination of captured network packets. By exploring these aspects, this paper aims to contribute to the existing body of knowledge in the field of computer forensics and support practitioners in their pursuit of effective investigative techniques.
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Phillips, P. Jonathon, Amy N. Yates, Ying Hu, et al. "Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms." Proceedings of the National Academy of Sciences 115, no. 24 (2018): 6171–76. http://dx.doi.org/10.1073/pnas.1721355115.
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Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.
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Putra, Surya Dwi, and Stanislaus Riyanta. "Digital Forensic Governance Strategy in Indonesia to Realize The Credibility of Accountable and Efficient Public Law Enforcement Agencies." Journal of Social Research 4, no. 7 (2025): 1316–27. https://doi.org/10.55324/josr.v4i7.2600.
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The rapid advancement of digital technology has made the use of digital forensic equipment essential for law enforcement in Indonesia, particularly by state institutions and government agencies. In today's digital era, nearly all human activities involve digital technology, necessitating the use of digital devices. When unlawful acts occur within the territory of Indonesia, digital forensic tools are vital for extracting data from devices, which can be utilized as evidence to uncover legal facts. This research aims to formulate strategies for managing digital forensics in an efficient, accountable, and credible manner. A qualitative approach was used, employing the literature study method to gather relevant data from various sources. The findings highlight the importance of establishing clear protocols for the use of digital forensic equipment, ensuring the accuracy and legality of data extraction. Additionally, the study emphasizes the need for proper training, certification, and standardization of practices among law enforcement agencies to enhance credibility and accountability. The research contributes to the development of strategic frameworks for managing digital forensics in Indonesia, providing a foundation for improving law enforcement practices in the digital age.
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Zuhriyanto, Ikhsan, Anton Yudhana, and Imam Riadi. "Comparative analysis of Forensic Tools on Twitter applications using the DFRWS method." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 5 (2020): 829–36. http://dx.doi.org/10.29207/resti.v4i5.2152.
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Current crime is increasing, one of which is the crime of using social media, although no crime does not leave digital evidence. Twitter application is a social media that is widely used by its users. Acts of crime such as fraud, insults, hate speech, and other crimes lately use many social media applications, especially Twitter. This research was conducted to find forensic evidence on the social media Twitter application that is accessed using a smartphone application using the Digital Forensics Research Workshop (DFRWS) method. These digital forensic stages include identification, preservation, collection, examination, analysis, and presentation in finding digital evidence of crime using the MOBILedit Forensic Express software and Belkasoft Evidence Center. Digital evidence sought on smartphones can be found using case scenarios and 16 variables that have been created so that digital proof in the form of smartphone specifications, Twitter accounts, application versions, conversations in the way of messages and status. This study's results indicate that MOBILedit Forensic Express digital forensic software is better with an accuracy rate of 85.75% while Belkasoft Evidence Center is 43.75%.
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Alamir, Ahmad, James Watterson, and Ibraheem Attafi. "Development and Validation of a Uplc-Qtof-Ms Method for Blood Analysis of Isomeric Amphetamine-Related Drugs." Separations 9, no. 10 (2022): 285. http://dx.doi.org/10.3390/separations9100285.
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The identification of isomeric drugs is gaining increasing importance in forensics and doping control. Isomers vary in terms of safety, effectiveness, and regulation, particularly for amphetamine-related drugs (ARDs). This study developed and validated a pseudo-isocratic UPLC-qTOF-MS analytical method for the identification of isomeric Amphetamine-related drugs (ARDs) in blood following mixed-mode solid-phase extraction (MMSPE). The procedure requires 250 μL of blood to achieve a limit of quantification (LOQ) and detection (LOD) of 20 ng/mL for all analytes. In aged animal blood samples, extraction recoveries of 63–90% and matrix effects of 9–21% were observed. Precision and accuracy for all analytes were within 20% and 89–118%, respectively. The analytical method was developed and validated in accordance with the Scientific Working Group for Forensic Toxicology (SWGTOX) Standard. It has acceptable accuracy and precision for use in doping control and forensic toxicology.
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Islam, Zahurul. "Strengthening Forensic Science in Bangladesh’s Criminal Justice System: Challenges, Opportunities, and Strategies for Improvement." International Journal of Research and Innovation in Social Science IX, no. XVI (2025): 21–34. https://doi.org/10.47772/ijriss.2024.916sco0003.
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Forensic science is integral to enhancing the accuracy, efficiency, and credibility of criminal investigations and legal proceedings. In Bangladesh, where the criminal justice system grapples with issues such as delayed trials, limited resources, and an overwhelming backlog of cases, the application of forensic science presents a critical opportunity for reform. This study explores the current role and potential of forensic science in Bangladesh’s criminal justice system, highlighting key forensic disciplines such as DNA analysis, fingerprinting, digital forensics, and toxicology. Through a comprehensive review of these disciplines, the research underscores their contribution to more reliable investigations, fairer trials, and enhanced public confidence in the justice system. Additionally, the study identifies several challenges hindering the effective implementation of forensic science, including inadequate infrastructure, a shortage of trained personnel, and low public awareness. Drawing on a combination of literature review, case studies, stakeholder interviews, and public surveys, the research evaluates the effectiveness of forensic science in law enforcement and judicial processes. The study proposes a series of recommendations to address these challenges, focusing on modernizing forensic capabilities, capacity-building, and fostering greater collaboration among forensic experts, law enforcement agencies, and the judiciary. Ultimately, the study aims to strengthen the role of forensic science in ensuring justice, by improving criminal investigation processes and reinforcing public trust in Bangladesh’s legal system.
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Shalgynbayeva, A. R. "COMPARATIVE ANALYSIS OF FORENSIC TOOLS: ENCASE AND FTK IMAGER." Bulletin of Shakarim University. Technical Sciences, no. 1(17) (March 29, 2025): 86–92. https://doi.org/10.53360/2788-7995-2025-1(17)-11.
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Disk forensics is an essential area of information security aimed at examining digital storage devices to identify, recover, and analyze data relevant to investigations. This study focuses on the functionality and performance of modern tools for disk forensics analysis, with a primary emphasis on comparing the capabilities of programs such as EnCase and FTK Imager used in this field.The research includes an overview of existing methods and technologies underlying forensic tools, including the search for deleted files, recovery of file systems, metadata analysis, and detection of artifacts indicating traces of user activity. In addition to functional capabilities, the study evaluates the performance of the software, including data processing speed, the range of supported file formats, and the efficiency of handling large volumes of information.Special attention is paid to quality assessment criteria for the tools, such as data recovery accuracy, user interface, and support for task automation.The results of the study may be valuable for professionals in cybersecurity, forensic investigation, and IT administration, as well as for students and researchers studying digital forensics. The work aims to enhance understanding of the capabilities and limitations of modern disk analysis tools, facilitating the selection of optimal solutions for specific tasks.
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Nahdli, Muhammad Fahmi Mubarok, Imam Riadi, and Muhammad Kunta Biddinika. "Comparison of Digital Forensic Tools for Drug Trafficking Cases on Instagram Messenger using NIST Method." Scientific Journal of Informatics 11, no. 4 (2024): 891–902. https://doi.org/10.15294/sji.v11i4.13463.
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Purpose: Cybercrime is an unlawful act that utilizes computer technology and the development of the internet. Cybercrime can occur on all electronic devices, including Android smartphones. Forensic handling, particularly mobile forensics, has become crucial in addressing drug trafficking cases conducted through Instagram. As the primary device for accessing Instagram, smartphones store digital data that can serve as evidence in investigations. This research aims to produce a more accurate comparison of results in analyzing Instagram Messenger data containing content related to drug trafficking. Methods: The digital evidence data used in this research included five types of data: text chat, account, image, audio, and image view once. The forensic tools for obtaining digital evidence were MOBILedit, Belkasoft, Mobile Forensic SPF, and Magnet Axiom. The method proposed in this research followed the NIST framework, which consists of four stages: collection, examination, analysis, and reporting. This research followed the NIST framework because it is widely recognized in the field of digital forensics and provides a comprehensive guideline for handling digital evidence. Result: Research results showed that Magnet Axiom had the best performance in digital forensic analysis, with a success rate of 74.1%. MOBILedit Forensic had a success rate of 62.5%, indicating lower performance. Mobile Forensic SPF had a success rate of 44.6%. In comparison, Belkasoft had the lowest success rate of 23.2%, showing that this software could be more effective in detecting and analyzing digital data than the others. Novelty: In this study, the analysis process was conducted using four digital forensic tools, each showing variations in terms of efficiency and effectiveness. Each tool has advantages and disadvantages regarding speed, accuracy, and ability to extract and manage data.
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Fauziah, Yessy Andriani, Eveline Yulia Darmadi, and Ryan Raditya Tjandra. "Integrating Digital Imaging and AI in Forensic Odontology: Enhancing Identification Accuracy and Efficiency." RSF Conference Series: Business, Management and Social Sciences 4, no. 1 (2024): 213–19. http://dx.doi.org/10.31098/bmss.v4i1.882.
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Background: The integration of digital imaging and artificial intelligence (AI) in forensic odontology can significantly enhance the accuracy and efficiency of dental identification processes. These technologies provide precise and rapid analysis, which is crucial for forensic investigations. However, implementing such technologies also introduces new ethical challenges that must be addressed. Purpose: To explore the synergistic use of digital imaging and artificial intelligence (AI) in forensic odontology. This section focuses on how these technologies improve identification accuracy and efficiency while discussing the ethical considerations that arise from their implementation. Methodology: This methodology involves a comprehensive review of the existing literature on the applications of digital imaging and AI in forensic odontology. Key studies, clinical trials, and case examples are analyzed to evaluate the effectiveness of these technologies in improving forensic outcomes. The review assesses these technologies’ impact on identification accuracy and efficiency while addressing ethical concerns such as data privacy and AI biases Results: The review highlights that the combination of digital imaging and AI significantly enhances the accuracy and speed of dental identification. Advanced algorithms can process large datasets and identify subtle patterns that are often missed by human examiners, resulting in more reliable forensic outcomes. However, ethical challenges, including data privacy concerns and potential biases in AI algorithms, are critical areas that require careful consideration and regulation. Ongoing research and policy development are essential to address these issues and ensure the ethical application of AI in forensic odontology.
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Michèle, Asengewe Aude, and Dr KN Jonathan. "Incorporating Mobile Forensic Tools into a Comprehensive Toolkit (Nugget)." Journal of Information and Technology 5, no. 3 (2025): 20–29. https://doi.org/10.70619/vol5iss3pp20-29.
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Mobile devices have become an essential part of everyday life, playing a crucial role in various activities. Their widespread use provides digital forensic investigators with valuable insights when analyzing cases. Given the vast amount of data stored on mobile devices, their significance in digital forensic investigations has grown substantially. However, forensic investigators face major challenges due to the diversity of tools and lack of standardization in data representation. To address these challenges, Nugget a Domain-Specific Language (DSL) for digital forensics was developed. Nugget provides a structured approach to defining forensic computations while abstracting technical implementation details. It enables investigators to describe operations on digital evidence without needing to manage the underlying execution. Despite its benefits, Nugget initially lacked support for mobile forensic investigations. This study aimed to enhance Nugget’s capabilities by integrating mobile forensic tools and extending its language to support mobile data analysis. Widely accessible forensic tools that support command-line execution on Android and iOS platforms were selected for integration. The implementation involved expanding Nugget’s grammar, incorporating forensic tool outputs via RPC, and validating the framework using forensic corpora. Key findings show that the integration improved the interoperability of forensic tools, reduced inconsistencies in data handling, and enhanced investigative workflows. Comparative analysis with traditional approaches revealed increased accuracy and decreased processing time. This research successfully extended Nugget to support mobile forensic investigations, creating a unified and standardized framework for analyzing mobile data. The proposed solution not only addresses current gaps in forensic tool integration but also lays the groundwork for future enhancements, including greater automation and compatibility with additional tools.
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Ulery, B. T., R. A. Hicklin, J. Buscaglia, and M. A. Roberts. "Accuracy and reliability of forensic latent fingerprint decisions." Proceedings of the National Academy of Sciences 108, no. 19 (2011): 7733–38. http://dx.doi.org/10.1073/pnas.1018707108.
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Romanos, Dr Paula. "Attorneys awareness of forensic science: A survey conducted in Lebanon." Polaris Global Journal of Scholarly Research and Trends 1, no. 1 (2022): 38–51. http://dx.doi.org/10.58429/pgjsrt.v1n1a86.
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The relative association between the forensic science and the law is increasingly under spot. Since forensics is the application of science in order to serve the law, many forensic evidence types are widely used in the criminal justice system such as DNA, toxicology, GSR, fingerprint, firearm. To assure a better implementation of forensic investigation procedures, the attorneys awareness of forensic science is crucially required. Moreover, an increased concern about the attorneys knowledge and perception regarding forensic based investigations should be addressed. Therefore, the aim of this study is to evaluate the level of awareness among Lebanese attorney community by assessing their views and needs regarding forensics. A cross sectional study was conducted over 60 Lebanese lawyers from both genders residing in the five Lebanese governorates. The survey based questionnaire was well designed and carried on from May 2022 to October 2022 with 83% as responsive rate. The participants characteristics were under study as well as their views and needs statements in the purpose of investigating their relative association through the independent t-test. According to the results, this study revealed that the majority of the participants were unaware of the forensic science notions. As for the participants features, the results showed that there is a significant difference of technology advances awareness among participants that underwent past trainings and those who did not. Nevertheless, there is no significant difference in the participants intention to participate in future trainings among participants that underwent past trainings and those who did not. This research findings illustrated that 85% of the volunteers were willing to participate in future trainings. In addition, this study showed that the DNA evidence was picked as the preferred topic to be covered in upcoming trainings. This is due mainly to its high reliability and accuracy. Although this survey identified that the Lebanese lawyers that accomplished past trainings were more aware of some forensics concepts, yet a larger sample size is needed. Thus, it is very early to decisively predict the level of forensics awareness among the Lebanese attorney community. By conclusion, trainings aimed to increase the attorneys awareness regarding the importance of forensics are necessary to reduce the gaps in their personal views and fulfill their needs in future trainings.
 Keywords: Attorneys awareness, Forensics, Lebanon, Needs, Views.
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Nishiket Waghmode, Pravin Bansode, Digambar Chalkapure, and Ms. Uttara Varade. "Solve the Mystery: DCGAN-Based Sketch to Real Face Conversion." International Research Journal on Advanced Engineering and Management (IRJAEM) 6, no. 07 (2024): 2259–63. http://dx.doi.org/10.47392/irjaem.2024.0328.
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This paper explores the advanced application of Artificial Intelligence (AI) in criminal identification through facial recognition, specifically by transforming forensic sketches into realistic photos using Deep Convolutional Generative Adversarial Networks (DCGAN). When a witness provides a description of a criminal, an expert creates a forensic sketch based on this description. By using DCGAN, this sketch is fed to into a neural network, which, after training, generates accurate, realistic facial images of the suspect. This technique significantly aids crime investigations by quickly producing detailed, high-resolution images from basic sketches, even those that are incomplete or depict various poses. The method is valuable in forensics, law enforcement, facial recognition, and security systems, enhancing the efficiency and accuracy of criminal identification
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Serketzis, Nikolaos, Vasilios Katos, Christos Ilioudis, Dimitrios Baltatzis, and Georgios Pangalos. "Improving Forensic Triage Efficiency through Cyber Threat Intelligence." Future Internet 11, no. 7 (2019): 162. http://dx.doi.org/10.3390/fi11070162.
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The complication of information technology and the proliferation of heterogeneous security devices that produce increased volumes of data coupled with the ever-changing threat landscape challenges have an adverse impact on the efficiency of information security controls and digital forensics, as well as incident response approaches. Cyber Threat Intelligence (CTI)and forensic preparedness are the two parts of the so-called managed security services that defendants can employ to repel, mitigate or investigate security incidents. Despite their success, there is no known effort that has combined these two approaches to enhance Digital Forensic Readiness (DFR) and thus decrease the time and cost of incident response and investigation. This paper builds upon and extends a DFR model that utilises actionable CTI to improve the maturity levels of DFR. The effectiveness and applicability of this model are evaluated through a series of experiments that employ malware-related network data simulating real-world attack scenarios. To this extent, the model manages to identify the root causes of information security incidents with high accuracy (90.73%), precision (96.17%) and recall (93.61%), while managing to decrease significantly the volume of data digital forensic investigators need to examine. The contribution of this paper is twofold. First, it indicates that CTI can be employed by digital forensics processes. Second, it demonstrates and evaluates an efficient mechanism that enhances operational DFR.
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Serketzis, Nikolaos, Vasilios Katos, Christos Ilioudis, Dimitrios Baltatzis, and George Pangalos. "Improving Forensic Triage Efficiency through Cyber Threat Intelligence." Future Internet 11, no. 00162 (2019): 15. https://doi.org/10.3390/fi11070162.
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The complication of information technology and the proliferation of heterogeneous security devices that produce increased volumes of data coupled with the ever-changing threat landscape challenges have an adverse impact on the efficiency of information security controls and digital forensics, as well as incident response approaches. Cyber Threat Intelligence (CTI)and forensic preparedness are the two parts of the so-called managed security services that defendants can employ to repel, mitigate or investigate security incidents. Despite their success, there is no known effort that has combined these two approaches to enhance Digital Forensic Readiness (DFR) and thus decrease the time and cost of incident response and investigation. This paper builds upon and extends a DFR model that utilises actionable CTI to improve the maturity levels of DFR. The effectiveness and applicability of this model are evaluated through a series of experiments that employ malware-related network data simulating real-world attack scenarios. To this extent, the model manages to identify the root causes of information security incidents with high accuracy (90.73%), precision (96.17%) and recall (93.61%), while managing to decrease significantly the volume of data digital forensic investigators need to examine. The contribution of this paper is twofold. First, it indicates that CTI can be employed by digital forensics processes. Second, it demonstrates and evaluates an efficient mechanism that enhances operational DFR.
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Rena Lela Putri and Septiawan Santana Kurnia. "Representasi Komunikasi Forensik pada Film Ice Cold." Bandung Conference Series: Public Relations 4, no. 2 (2024): 635–41. http://dx.doi.org/10.29313/bcspr.v4i2.13412.
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Abstract. The murder case of Mirna Salihin, which sparked controversy and inspired the making of the documentary film "Ice Cold." This film attempts to depict the chronology of the murder and the lengthy trial, with the aim of providing a broader perspective to the audience. This research will analyze how forensic communication is represented in the film, focusing on the messages related to forensic communication conveyed and interpreted in the film. This study aims to analyze the representation of forensic communication in the film "Ice Cold" using Roland Barthes' semiotic approach.The research methodology utilized a constructivist paradigm with a qualitative approach and data collection techniques involving observation, interviews, and literature review. The research subject is the film "Ice Cold," while the research objects encompass coffee symbolism, forensic sign analysis, visual representation, dialogue, and social context within the film. Primary data was obtained through direct observation of the film, while secondary data originated from relevant literature.Data analysis stages involved data reduction, data presentation, and data verification. Data validity was examined through technical triangulation and observer triangulation. The research findings are expected to provide a profound understanding of how the film "Ice Cold" represents forensic communication and its implications on the public's perception and evaluation of the murder case. Based on the discussion in this study, the researcher concludes several results as follows: (1) The denotative meaning shows the forensic communication process in detail through gestures, expressions, and the presentation of objective scientific evidence, depicting the accuracy of the forensic process in the film. (2) The connotative meaning portrays the complexity, pressure, and uncertainty involved in the forensic process and their impact on the perception and emotional reactions of the characters. (3) The film presents various societal myths about legal and forensic processes, such as the absolute certainty of scientific evidence and the transparency of investigations. (4) The forensic process is not only about finding factual truth but also about maintaining integrity, thoroughness, and honesty in the disclosure of evidence. (5) The representation in the film influences public understanding of the complexity and challenges of the forensic process, encouraging a deeper and more critical understanding of criminal investigation procedures. Abstrak. Kasus pembunuhan Mirna Salihin yang menimbulkan kontroversi dan menginspirasi pembuatan film dokumenter "Ice Cold". Film ini mencoba menggambarkan kronologi peristiwa pembunuhan tersebut serta pengadilan yang berlangsung lama, dengan tujuan memberikan perspektif yang lebih luas kepada penonton. Penelitian ini akan menganalisis bagaimana komunikasi forensik direpresentasikan dalam film tersebut, dengan fokus pada pesan-pesan terkait komunikasi forensik yang disampaikan dan diinterpretasikan dalam film. Penelitian ini bertujuan untuk menganalisis representasi komunikasi forensik dalam film "Ice Cold" dengan menggunakan pendekatan semiotika Roland Barthes. Metode penelitian yang digunakan adalah paradigma konstruktivis dengan pendekatan kualitatif dan teknik pengumpulan data berupa observasi, wawancara, dan studi pustaka. Subjek penelitian adalah film "Ice Cold," sementara objek penelitian mencakup simbolisme kopi, analisis tanda forensik, representasi visual, dialog, dan konteks sosial dalam film. Data primer diperoleh melalui observasi langsung terhadap film, sedangkan data sekunder berasal dari literatur terkait.Tahap analisis data melibatkan reduksi data, penyajian data, dan verifikasi data. Keabsahan data diuji melalui triangulasi teknis dan triangulasi pengamat. Hasil penelitian menunjukkan bahwa (1) Makna denotasi menunjukkan proses komunikasi forensik secara detail melalui gestur, ekspresi, dan presentasi bukti ilmiah yang objektif, menggambarkan akurasi proses forensik dalam film. (2) Makna konotasi menggambarkan kompleksitas, tekanan, dan ketidakpastian dalam proses forensik serta dampaknya terhadap persepsi dan reaksi emosional karakter. (3) Film ini mengangkat berbagai mitos masyarakat tentang proses hukum dan forensik, seperti kepastian absolut dari bukti ilmiah dan transparansi investigasi. (4) Proses forensik tidak hanya tentang menemukan kebenaran faktual, tetapi juga mempertahankan integritas, ketelitian, dan kejujuran dalam pengungkapan bukti. (5) Representasi dalam film mempengaruhi pemahaman masyarakat tentang kompleksitas dan tantangan proses forensik, mendorong pemahaman yang lebih mendalam dan kritis terhadap proses investigasi kriminal.
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Riadi, Imam, Herman, and Nur Hamida Siregar. "Mobile Forensic Analysis of Signal Messenger Application on Android using Digital Forensic Research Workshop (DFRWS) Framework." Ingénierie des systèmes d information 27, no. 6 (2022): 903–13. http://dx.doi.org/10.18280/isi.270606.
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Cybercrime is a crime committed using equipment connected to the internet. One of the cybercrimes that occured during the COVID-19 pandemic was the spread hoaxes about the COVID-19 vaccine which caused panic in society. Signal Messenger is one of the social media that has become a trending topic since the number of personal data security issues and the emergence of end-to-end encryption features. This research aims to find digital evidence on Signal Messenger application installed on the perpetrator's Android smartphone. This research uses Belkasoft, Magnet AXIOM, and MOBILedit Forensic Express tools and implements the Digital Forensics Research Workshop (DFRWS) framework in each stage of the research experiment. The research was carried out according to the case scenario with 11 predetermined parameters. Digital evidence is found from the Signal Messenger application: application information, account information, chat, pictures, videos, contacts, and stickers. The results of this research indicate that Belkasoft Evidence Center forensic tool is better, with an accuracy rate of 78.69%, while Magnet AXIOM is 26.23% and MOBILedit Forensic Express is 9.84%. The results of this research can be used as a reference for other forensic researchers/experts in handling similar crime cases on the Signal Messenger application to get better results.
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Wibowo, Mukti, Mohammad Raffli Firmansyah, and Rezar Surya Efendi. "ANALISIS BUKTI DIGITAL PADA APLIKASI DISCORD DESKTOP DENGAN MENGGUNAKAN FRAMEWORK DFRWS." JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI 15, no. 1 (2024): 98–111. http://dx.doi.org/10.51903/jtikp.v15i1.826.
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A Currently, various communication platforms are available, and one of them is Discord. Discord is utilized by users to interact and share various types of content. However, Discord is often implicated in the distribution of inappropriate content that goes against Discord regulations. This can lead to cybercrime cases such as Child Sexual Abuse Material (CSAM). The main challenge faced is the recovery of digitally deleted data through these communication platforms. The objective of this research is to analyze digital evidence in the form of deleted text messages, images, and documents in the chat rooms of the desktop-based Discord application. The research method employed is the Live Forensic Technique, applying the DFRWS (Digital Forensics Research Workshop) method. The digital forensic analysis process is conducted using the digital forensic tool Magnet Axiom. The research results indicate that the accuracy of digital evidence analysis using Magnet Axiom reaches 94.11%. A total of 16 digital evidence items were successfully recovered out of the total 17 items available
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Elliethy, Ahmed. "Neural noiseprint transfer: a generic noiseprint-based counter forensics framework." PeerJ Computer Science 9 (April 27, 2023): e1359. http://dx.doi.org/10.7717/peerj-cs.1359.
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A noiseprint is a camera-related artifact that can be extracted from an image to serve as a powerful tool for several forensic tasks. The noiseprint is built with a deep learning data-driven approach that is trained to produce unique noise residuals with clear traces of camera-related artifacts. This data-driven approach results in a complex relationship that governs the noiseprint with the input image, making it challenging to attack. This article proposes a novel neural noiseprint transfer framework for noiseprint-based counter forensics. Given an authentic image and a forged image, the proposed framework synthesizes a newly generated image that is visually imperceptible to the forged image, but its noiseprint is very close to the noiseprint of the authentic one, to make it appear as if it is authentic and thus renders the noiseprint-based forensics ineffective. Based on deep content and noiseprint representations of the forged and authentic images, we implement the proposed framework in two different approaches. The first is an optimization-based approach that synthesizes the generated image by minimizing the difference between its content representation with the content representation of the forged image while, at the same time, minimizing the noiseprint representation difference from the authentic one. The second approach is a noiseprint injection-based approach, which first trains a novel neural noiseprint-injector network that can inject the noiseprint of an image into another one. Then, the trained noiseprint-injector is used to inject the noiseprint from the authentic image into the forged one to produce the generated image. The proposed approaches are generic and do not require training for specific images or camera models. Both approaches are evaluated on several datasets against two common forensic tasks: the forgery localization and camera source identification tasks. In the two tasks, the proposed approaches are able to significantly reduce several forensic accuracy scores compared with two noiseprint-based forensics methods while at the same time producing high-fidelity images. On the DSO-1 dataset, the reduction in the forensic accuracy scores has an average of 75%, while the produced images have an average PSNR of 31.5 dB and SSIM of 0.9. The source code of the proposed approaches is available on GitHub (https://github.com/ahmed-elliethy/nnt).
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Nortje, Jacobus Gerhardus, and Daniel Christoffel Myburgh. "The Search and Seizure of Digital Evidence by Forensic Investigators in South Africa." Potchefstroom Electronic Law Journal 22 (April 25, 2019): 1–42. http://dx.doi.org/10.17159/1727-3781/2019/v22i0a4886.
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The discipline of digital forensics requires a combination of skills, qualifications and knowledge in the area of forensic investigation, legal aspects and information technology. The uniqueness of digital evidence makes the adoption of traditional legal approaches problematic.
 Information technology terminology is currently used interchangeably without any regard to being unambiguous and consistent in relation to legal texts. Many of the information technology terms or concepts have not yet achieved legal recognition.
 The recognition and standardisation of terminology within a legal context are of the utmost importance to ensure that miscommunication does not occur.
 To provide clarity or guidance on some of the terms and concepts applicable to digital forensics and for the search and seizure of digital evidence, some of the concepts and terms are reviewed and discussed, using the Criminal Procedure Act 51 of 1977 as a point of departure.
 Digital evidence is often collected incorrectly and analysed ineffectively or simply overlooked due to the complexities that digital evidence poses to forensic investigators. As with any forensic science, specific regulations, guidelines, principles or procedures should be followed to meet the objectives of investigations and to ensure the accuracy and acceptance of findings. These regulations, guidelines, principles or procedures are discussed within the context of digital forensics: what processes should be followed and how these processes ensure the acceptability of digital evidence. These processes include international principles and standards such as those of the Association of Chiefs of Police Officers and the International Organisation of Standardisation. A summary is also provided of the most influential or best-recognised international (IOS) standards on digital forensics.
 It is concluded that the originality, reliability, integrity and admissibility of digital evidence should be maintained as follows:
 
 Data should not be changed or altered.
 Original evidence should not be directly examined.
 Forensically sound duplicates should be created.
 Digital forensic analyses should be performed by competent persons.
 Digital forensic analyses should adhere to relevant local legal requirements.
 Audit trails should exist consisting of all required documents and actions.
 The chain of custody should be protected.
 Processes and procedures should be proper, while recognised and accepted by the industry.
 
 If the ACPO (1997) principles and ISO/IEC 27043 and 27037 Standards are followed as a forensic framework, then digital forensic investigators should follow these standards as a legal framework.
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Sawarkar, Ankush D., and Anjali Deepak Hazar. "IoT Forensic Cyber Activities Detection and Prevention with Automated Machine Learning Model." Journal of Sensors, IoT & Health Sciences 2, no. 2 (2024): 1–15. http://dx.doi.org/10.69996/jsihs.2024006.
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The Internet of Things (IoT) has been deployed in a vast range of applications with exponential increases in data size and complexity. Existing forensic techniques are not effective for the accuracy and detection rate of security issues in IoT forensics. Cyber forensic comprises huge volume constraints that are processing huge volumes of data in the Information and Communication Technology (ICT) comprised of IoT devices and platforms. Trust blockchain is effective technology those are utilized to assess the tamper-proof records in all transaction in the IoT environment. With the implementation of trust blockchain the record and transaction are processed with a distributed ledger that is managed by the network nodes. The challenge associated with the trust blockchain in IoT forensics is cost and security. To achieve significant cost-effectiveness organizations, need to evaluate the risks and benefits associated with IoT forensics in the trust blockchain technology. In this paper, developed a Block Chain Enabled Cyber-Physical system with distributed storage. The developed Blockchain model is termed as Integrated Hadoop Blockchain Forensic Machin Learning (IHBF-ML). The IHBF-ML model uses the Hadoop Distributed File System (HDFS) with cyberspace to improve security. Within the IHBF-ML model, IoT data communication is established with the smart contract. The smart contract-based blockchain process uses the Machine Learning model integrated with Cat Boost classification model for anomaly detection. Cost in IoT forensic is minimized with the parallel processing of the data through MapReduce Framework for the traffic translation, extraction, and analysis of the dynamic feature traffic from the IoT environment. The experimental analysis stated that constructed IHBF-ML model reduces the cost by ~25% than the other conventional blockchain Ethereum and EOS.