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Premanand Narasimhan and Dr. N. Kala. "Emerging Trends in Digital Forensics : Investigating Cybercrime." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 3645–52. https://doi.org/10.32628/cseit251451.
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Digital forensics is a rapidly evolving field that plays a critical role in investigating cybercrime, data breaches, and illicit activities across various domains, including blockchain, cryptocurrency, and the dark web. This paper explores key areas of digital forensics, including computer forensics, mobile forensics, network forensics, cloud forensics, IoT forensics, and embedded system forensics. Emerging trends such as drone and satellite forensics highlight the increasing scope of forensic investigations beyond traditional computing environments. Additionally, the study delves into blockchain forensics, which focuses on tracing cryptocurrency transactions to combat money laundering, ransomware payments, and illicit trading on the dark web. Advanced tools such as Chainalysis, Maltego, and SpiderFoot are employed in forensic methodologies to track digital evidence effectively. The paper also addresses challenges such as encryption, data volatility, jurisdictional barriers, and anti-forensics techniques used by cybercriminals. Legal and compliance issues, including GDPR, HIPAA, and ISO 27037, are also discussed in the context of admissibility and cross-border investigations. By analyzing real-world case studies—including the Silk Road takedown, Sony Pictures hack, and AlphaBay shutdown—this paper provides insight into the role of forensic experts in digital investigations. With advancements in artificial intelligence and machine learning, digital forensics continues to evolve, offering law enforcement and cybersecurity professionals new techniques to trace digital footprints and counter cyber threats effectively.
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DOVHAN, O., and T. TKACHUK. "Combating counter-forensics as a component of digital security of critical infrastructure of Ukraine." INFORMATION AND LAW, no. 2(53) (June 24, 2025): 115–25. https://doi.org/10.37750/2616-6798.2025.2(53).334135.
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The article examines counter-forensics as one of the most dangerous forms of digital interference, which poses a direct threat to the digital security of critical infrastructure of Ukraine in the conditions of a full-scale war. It is substantiated that counter-forensic technologies — including wiping, fileless attacks, steganography, deepfakes, and other tools designed to conceal digital traces — are actively employed in contemporary cyberattacks targeting the stability of critical elements of state infrastructure. The paper provides an interdisciplinary analysis of the technical, legal and organizational aspects of combating counter-forensics. Real cases of interference in the work of energy, logistics, © Довгань О.Д., Ткачук Т.Ю., 2025 telecommunications and administrative systems are considered, during which attackers attempted to hide or destroy digital evidence. Special attention is paid to national and international experience in protecting digital traces, in particular, methods of backup storage of logs, the use of analytical tools based on artificial intelligence, institutional models of cooperation between state and private structures. The article contains proposals for improving the regulatory and legal regulation of electronic evidence for critical infrastructure, as well as recommendations for the technical re-equipment of forensic and operational infrastructure. The feasibility of creating a National Archive of Digital Evidence and introducing mandatory certification of digital forensics specialists, taking into account the counter-forensics component, is substantiated. It is summarized that effective counteraction to counter-forensics is not only a matter of criminal prosecution, but also a key condition for the functional resilience of critical infrastructure and digital security of the state in conditions of armed aggression.
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Keenan, Thomas. "Counter-forensics and Photography." Grey Room 55 (April 2014): 58–77. http://dx.doi.org/10.1162/grey_a_00141.
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Kang, Xiangui, Jingxian Liu, Hongmei Liu, and Z. Jane Wang. "Forensics and counter anti-forensics of video inter-frame forgery." Multimedia Tools and Applications 75, no. 21 (2015): 13833–53. http://dx.doi.org/10.1007/s11042-015-2762-7.
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Yang, Pengpeng, Daniele Baracchi, Rongrong Ni, Yao Zhao, Fabrizio Argenti, and Alessandro Piva. "A Survey of Deep Learning-Based Source Image Forensics." Journal of Imaging 6, no. 3 (2020): 9. http://dx.doi.org/10.3390/jimaging6030009.
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Image source forensics is widely considered as one of the most effective ways to verify in a blind way digital image authenticity and integrity. In the last few years, many researchers have applied data-driven approaches to this task, inspired by the excellent performance obtained by those techniques on computer vision problems. In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics. To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification. Moreover, we have included the works on anti-forensics and counter anti-forensics. For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field.
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Irons, Alastair, and Jacques Ophoff. "Aspects of Digital Forensics in South Africa." Interdisciplinary Journal of Information, Knowledge, and Management 11 (2016): 273–83. http://dx.doi.org/10.28945/3576.
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This paper explores the issues facing digital forensics in South Africa. It examines particular cyber threats and cyber threat levels for South Africa and the challenges in addressing the cybercrimes in the country through digital forensics. The paper paints a picture of the cybercrime threats facing South Africa and argues for the need to develop a skill base in digital forensics in order to counter the threats through detection of cybercrime, by analyzing cybercrime reports, consideration of current legislation, and an analysis of computer forensics course provision in South African universities. The paper argues that there is a need to develop digital forensics skills in South Africa through university programs, in addition to associated training courses. The intention in this paper is to promote debate and discussion in order to identify the cyber threats to South Africa and to encourage the development of a framework to counter the threats – through legislation, high tech law enforcement structures and protocols, digital forensics education, digital forensics skills development, and a public and business awareness of cybercrime threats.
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Amit, Kapoor, and Vinod Mahor Prof. "Broadcasting Forensics Using Machine Learning Approaches." International Journal of Trend in Scientific Research and Development 7, no. 3 (2023): 1034–45. https://doi.org/10.5281/zenodo.8068297.
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Broadcasting forensic is the practice of using scientific methods and techniques to analyse and authenticate Multimedia content. Over the past decade, consumer-grade imaging sensors have become increasingly prevalent, generating vast quantities of images and videos that are used for various public and private communication purposes. Such applications include publicity, advocacy, disinformation, and deception, among others. This paper aims to develop tools that can extract knowledge from these visuals and comprehend their provenance. However, many images and videos undergo modification and manipulation before public release, which can misrepresent the facts and deceive viewers. To address this issue, we propose a set of forensics and counter-forensic techniques that can help establish the authenticity and integrity of Multimedia content. Additionally, we suggest ways to modify the content intentionally to mislead potential adversaries. Our proposed tools are evaluated using publicly available datasets and independently organized challenges. Our results show that the forensics and counter-forensic techniques can accurately identify manipulated content and can help restore the original image or video. Furthermore, in this paper demonstrate that the modified content can successfully deceive potential adversaries while remaining undetected by state-of-the-art forensic methods.
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Ahmed, Abdulghani Ali, Khalid Farhan, Waheb A. Jabbar, Abdulaleem Al-Othmani, and Abdullahi Gara Abdulrahman. "IoT Forensics: Current Perspectives and Future Directions." Sensors 24, no. 16 (2024): 5210. http://dx.doi.org/10.3390/s24165210.
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The Internet of Things forensics is a specialised field within digital forensics that focuses on the identification of security incidents, as well as the collection and analysis of evidence with the aim of preventing future attacks on IoT networks. IoT forensics differs from other digital forensic fields due to the unique characteristics of IoT devices, such as limited processing power and connectivity. Although numerous studies are available on IoT forensics, the field is rapidly evolving, and comprehensive surveys are needed to keep up with new developments, emerging threats, and evolving best practices. In this respect, this paper aims to review the state of the art in IoT forensics and discuss the challenges in current investigation techniques. A qualitative analysis of related reviews in the field of IoT forensics has been conducted, identifying key issues and assessing primary obstacles. Despite the variety of topics and approaches, common issues emerge. The majority of these issues are related to the collection and pre-processing of evidence because of the counter-analysis techniques and challenges associated with gathering data from devices and the cloud. Our analysis extends beyond technological problems; it further identifies the procedural problems with preparedness, reporting, and presentation as well as ethical issues. In particular, it provides insights into emerging threats and challenges in IoT forensics, increases awareness and understanding of the importance of IoT forensics in preventing cybercrimes, and ensures the security and privacy of IoT devices and networks. Our findings make a substantial contribution to the field of IoT forensics, as they not only involve a critical analysis of the challenges presented in existing works but also identify numerous problems. These insights will greatly assist researchers in identifying appropriate directions for their future research.
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Fuller, Matthew, and Nikita Mazurov. "A Counter-Forensic Audit Trail: Disassembling the Case of The Hateful Eight." Theory, Culture & Society 36, no. 6 (2019): 171–96. http://dx.doi.org/10.1177/0263276419840418.
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Forensics is proposed as a means to understand, trace, and recompile data and computational activities. It has a securitocratic dimension and one that is being developed as a means of opening processes, events and systems into a more public state. This article proposes an analysis of forces at play in the circulation of a ‘screener’ of Quentin Tarantino’s The Hateful Eight and associated files, to suggest that forensic approaches used to control flows of data may be repurposed for dissemination. The article maps a brief history of digital forensics and sets out some of its political entailments, indicating further lines of enquiry regarding the inter-relation of technosocial powers constituted in the interactions between forensics and counter-measures. The article proposes that the posthumanities are partially constituted by a renewed relationship between questions of culture, subjectivity, knowledge and the technical. Some propositions for the technical as grounds for cultural politics are made.
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Imeri Saiti, Deshira, Mentor Hamiti, and Jehona Asani. "Handling Data Security in Education." International Journal of Education & Well-Being 2, no. 2 (2024): 108–22. https://doi.org/10.62416/ijwb-41.
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Digital forensics is a new science and follows the rapid changes in the computer environment, expanding into many disciplines, which turns it into a very challenging field that requires the continuous development of methodologies and tools to counter the ever-newer changes in cybercrime. The research subject refers to the analysis and challenges of digital forensics, tracking of hard drives, recovery of deleted files from the hard drive, their quality after recovery, and the possibility of creating forensic images on physically damaged hard drives. The research is based on digital forensics and tracking of all hard drives by creating digital images and analyzing forensic images FTK Imager and Autopsy 4.15.0. The research concludes that the number of files allocated to a forensic image does not play a role in the duration of report generation during the creation of forensic images using FTK Imager. Unlike allocated files, unanalyzed files in a forensic image are directly proportional to the duration of generating reports for forensic images through FTK Imager. Also, in conclusion, it is worth noting that FTK Imager cannot generate a forensic image when the hard drive is physically damaged. This research paper investigates the integral relationship between data security and data recovery even after deletion, highlighting the importance of digital forensics in hard drive security management to protect sensitive information and maintain regulatory compliance, which should also find the right place in education. Educational institutions must balance early education on data permanence and recovery with ensuring students have foundational digital knowledge before introducing complex security concepts.
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Iqbal, Saima, Wilayat Khan, Abdulrahman Alothaim, Aamir Qamar, Adi Alhudhaif, and Shtwai Alsubai. "Proving Reliability of Image Processing Techniques in Digital Forensics Applications." Security and Communication Networks 2022 (March 31, 2022): 1–17. http://dx.doi.org/10.1155/2022/1322264.
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Binary images have found its place in many applications, such as digital forensics involving legal documents, authentication of images, digital books, contracts, and text recognition. Modern digital forensics applications involve binary image processing as part of data hiding techniques for ownership protection, copyright control, and authentication of digital media. Whether in image forensics, health, or other fields, such transformations are often implemented in high-level languages without formal foundations. The lack of formal foundation questions the reliability of the image processing techniques and hence the forensic results loose their legal significance. Furthermore, counter-forensics can impede or mislead the forensic analysis of the digital images. To ensure that any image transformation meet high standards of safety and reliability, more rigorous methods should be applied to image processing applications. To verify the reliability of these transformations, we propose to use formal methods based on theorem proving that can fulfil high standards of safety. To formally investigate binary image processing, in this paper, a reversible formal model of the binary images is defined in the Proof Assistant Coq. Multiple image transformation methods are formalized and their reliability properties are proved. To analyse real-life RGB images, a prototype translator is developed that reads RGB images and translate them to Coq definitions. As the formal definitions and proof scripts can be validated automatically by the computer, this raises the reliability and legal significance of the image forensic applications.
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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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Muthusamy, Dr P., Shanmugam V, Kapilsurya R, and Saran Kumar R. "Python-Based Security Operations Center (SOC) and Forensics Analysis for Incident Cyber Threats." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 2592–96. http://dx.doi.org/10.22214/ijraset.2024.60403.
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Abstract: The increasing complexity and frequency of cyber threats demand robust solutions for incident detection, analysis, and response. Security Operations Centers (SOCs) play a pivotal role in safeguarding organizational assets by monitoring, detecting, and mitigating cyber threats. This paper presents a Python-based approach for enhancing SOC capabilities and conducting forensics analysis to counter incident cyber threats effectively. Leveraging Python's versatility and extensive libraries, this research proposes a comprehensive framework that integrates various cybersecurity tools and techniques for real-time threat monitoring, incident analysis, and forensic investigation. The proposed solution empowers SOCs to detect and respond to cyber threats promptly while facilitating in-depth forensic examination for post-incident analysis and remediation. Through case studies and evaluations, the effectiveness and efficiency of the Python-based SOC and forensics analysis approach are demonstrated, highlighting its potential to enhance organizational cyber resilience.
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Chlebowicz, Piotr, and Tomasz Safjański. "Considerations Regarding the Typology of Counter-Detection Measures in the Light of Quantitative Research on Organised Crime Groups that Recruit Football Hooligans." Białostockie Studia Prawnicze 29, no. 4 (2024): 161–82. https://doi.org/10.15290/bsp.2024.29.04.10.
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Abstract Through the application of counter-detection activities, football hooligan groups in Poland have created organised crime networks, that are able to identify, locate and neutralise police intelligence activities. In the 20th and 21st centuries, the issue of counter-detection activities undertaken by members of organised crime groups has not been acknowledged by researchers. Therefore, the existing body of work on forensic tactics as a scientific discipline needs to be critically analysed, and new definitions need to be adopted. The scope of the study includes definitions of forms of counter-detection activities (e.g. counter-surveillance or inverse surveillance), as well as the classification and functions of counter-detection activities. The article seeks to establish a framework and define the conceptual grid and key assumptions underlying the concept of counter-detection activities. It is the first desk-research analysis to systematise knowledge on the counter-detection activities of criminal groups. Analysis results are the basis for the creation of a theory of anti-forensics,: its typology is presented with the example of groups of football hooligans. The authors define the concept of counter-detection activities and its purpose, and seek to delineate the basic forms and strategies of counter-detection. The knowledge presented is also referred to as ‘anti-forensics’, which in fact, is a specific area of knowledge on how to prevent the detection of crimes and criminals. This follows directly from the wording of the cardinal rule of all crime-fighting: ‘Think like a criminal’.
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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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Stevanović, Miroslav, and Dragan Đurđević. "The role of computer forensics in the fight against terrorism." Megatrend revija 17, no. 1 (2020): 129–42. http://dx.doi.org/10.5937/megrev2001129s.
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In this paper, the authors examine the adequacy of the counter-terrorism concept, which does not envisage institutional responsibility for collecting, processing, and fixing traces of cyber-related terrorist activities. The starting point is the fact that today numerous human activities and communication take place in the cyberspace. Firstly, the focus is on the aspects of terrorism that present a generator of challenges to social stability and, in this context, the elements of the approach adopted by the current National Security Strategy of the Republic of Serbia. In this analysis, adequacy is evaluated from the point of view of functionality. In this sense, it is an attempt to present elements that influence the effectiveness of counter-terrorism in the information age. Related to this is the specification of the role that digital forensics can play in this area. The conclusion is that an effective counter-terrorism strategy must necessarily encompass the institutional incorporation of digital forensics since it alone can contribute to the timely detection or assertion of responsibility for terrorism in a networked computing environment.
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Baracchi, Daniele, Dasara Shullani, Massimo Iuliani, Damiano Giani, and Alessandro Piva. "Camera Obscura: Exploiting in-camera processing for image counter forensics." Forensic Science International: Digital Investigation 38 (September 2021): 301213. http://dx.doi.org/10.1016/j.fsidi.2021.301213.
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De Rosa, Alessia, Marco Fontani, Matteo Massai, Alessandro Piva, and Mauro Barni. "Second-Order Statistics Analysis to Cope With Contrast Enhancement Counter-Forensics." IEEE Signal Processing Letters 22, no. 8 (2015): 1132–36. http://dx.doi.org/10.1109/lsp.2015.2389241.
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Doyoddorj, Munkhbaatar, and Kyung-Hyune Rhee. "A Targeted Counter-Forensics Method for SIFT-Based Copy-Move Forgery Detection." KIPS Transactions on Computer and Communication Systems 3, no. 5 (2014): 163–72. http://dx.doi.org/10.3745/ktccs.2014.3.5.163.
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Venkata, Udaya Sameer, and Ruchira Naskar. "Blind Image Source Device Identification." International Journal of Information Security and Privacy 12, no. 3 (2018): 84–99. http://dx.doi.org/10.4018/ijisp.2018070105.
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This article describes how digital forensic techniques for source investigation and identification enable forensic analysts to map an image under question to its source device, in a completely blind way, with no a-priori information about the storage and processing. Such techniques operate based on blind image fingerprinting or machine learning based modelling using appropriate image features. Although researchers till date have succeeded to achieve extremely high accuracy, more than 99% with 10-12 candidate cameras, as far as source device prediction is concerned, the practical application of the existing techniques is still doubtful. This is due to the existence of some critical open challenges in this domain, such as exact device linking, open-set challenge, classifier overfitting and counter forensics. In this article, the authors identify those open challenges, with an insight into possible solution strategies.
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Mehrish, Ambuj, A. V. Subramanyam, and Sabu Emmanuel. "Joint Spatial and Discrete Cosine Transform Domain-Based Counter Forensics for Adaptive Contrast Enhancement." IEEE Access 7 (2019): 27183–95. http://dx.doi.org/10.1109/access.2019.2901345.
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Ramirez, Fanny. "The digital divide in the US criminal justice system." New Media & Society 24, no. 2 (2022): 514–29. http://dx.doi.org/10.1177/14614448211063190.
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The growing use of digital evidence from smartphones and social media has led to a digital divide in the US criminal justice system that advantages law enforcement and prosecutors while further increasing the vulnerability of poor people and people of color who rely on public legal assistance. Drawing on a year-long ethnographic study of one of the first digital forensics laboratories in a public defender office, I argue that digital inclusion in the form of better resources for public defenders is necessary for equitable and fair representation in today’s criminal justice system. Findings show that access to digital forensic technologies is an important equalizing tool that allows public defenders to (1) mount strong, data-driven cases; (2) create counter narratives that challenge depictions of marginalized defendants as dangerous; and (3) engage in nuanced storytelling to highlight the complexities of human relationships and life circumstances that shape cases.
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Łabuz, Paweł, and Tomasz Safjański. "Counter-detection activities of criminal organizations aimed at reducing the effectiveness of surveillance conducted as part of operational activities." Issues of Forensic Science 298 (2017): 62–68. http://dx.doi.org/10.34836/pk.2017.298.3.
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The article presents the essential aspects of tactics and techniques applied by criminals with an aim to reduce the effectiveness of surveillance conducted as part of operational activities. The possible actions adopted by criminals with the purpose of preventing surveilling authorities from detecting their activities are characterized. The above issues are exceptionally complicated, owing to the specifics of the activities to be discussed. To date, counter-detection activities of criminal organizations have not been within the main area of interest for forensics. This article points out the advantage of having comprehensive knowledge of criminal tactics and techniques used in this field.
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Sorokin, Siim. "Plausibility Under Duress: Counter-Narrative, Suspicion and Folk Forensic Contra-Plotting." Narrative Works 13, no. 1 (2024): 127–47. https://doi.org/10.7202/1115727ar.
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How would it portend to analytical contextualization as well as specific theorization when instances where narrative kernels, once weaved into alternative epistemologies, make their way into, and become (re-)”plotted” on an inherently political platform, a session of state Parliament? Motivated by such an inquiry, the present multidisciplinary paper develops its theoretical argument by interrogating the notions of “counter-narrating” and “counter-narrative” cast on the intertwined conceptual landscape of forensics, tracking, and suspicion. The theoretical discussion is advanced further by developing the notions of productive suspicion and contra-plotting. On analytical level, the present chapter maintains that the narrative structure of some parliamentary discourses (presentations, Q&As) may operate much in the same manner as an anonymous forum thread or a reply chain in news’ commentaries. In undertaking this multidisciplinary theoretical discussion and analysis, the aim of this paper is to inform and expand the scholarship on counter-narratives and, in particular, to further solidify the conceptual aspects of the act, or practice, of counter-narrating.
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Łabuz, Paweł, and Tomasz Safjański. "Counter-detection activities of criminal groups aimed at limiting the effectiveness of operational and procedural control and interception of conversations." Issues of Forensic Science 297 (2017): 72–78. http://dx.doi.org/10.34836/pk.2017.297.4.
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The article presents the essential aspects of tactics and techniques applied by criminals with an aim to reduce the effectiveness of procedural eavesdropping and operational control. The most significant methods of protecting criminal correspondence were characterized. The above issues are exceptionally complicated, owing to the specifics of the activities to be discussed. To date, counter-detection activities of criminal organizations have not been within the main area of interest for forensics. The article highlights the benefits resulting from the knowledge of criminal tactics and techniques used to ensure the confidentiality of correspondence, in particular, in view of the ongoing legislative work pertaining to prosecutorial control exerted over operational and exploratory activities.
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Dave, Aparna, Pearl Gupta, Manpreet Arora, and Pulin Saluja. "Dental Record-keeping: Reviving the Importance." Indian Journal of Dental Sciences 17, no. 1 (2025): 38–41. https://doi.org/10.4103/ijds.ijds_123_24.
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Dental records are important documents in possession with dentists and are a fundamental requirement in delivering optimal health care. In addition to this, it is an important legal document which needs to be maintained appropriately. These records are essential components of patient care as they contain information about the patient, the opinion of the dentist, the treatment plan, the possible alternative treatment, patients’ consent, and details of possible associated complications. Adequate records help to counter the objections raised by patients. They also have a very important role in forensics where these records could help in the identification of a person. Dental records can also be utilized in teaching and can play an important role in research.
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Manisha, Chang-Tsun Li, Xufeng Lin, and Karunakar A. Kotegar. "Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification." Sensors 22, no. 20 (2022): 7871. http://dx.doi.org/10.3390/s22207871.
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Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.
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Venkatesh, M. "A Novel Digital Audio Encryption and Watermarking Scheme." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem.ncft044.
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Abstract—To enhance the privacy and security of audio signals stored in third-party storage centers, a robust digital audio encryption and forensics watermarking scheme is proposed. The scheme incorporates the AES-GCM (Advanced Encryption Standard in Galois/Counter Mode) algorithm for authenticated encryption, ensuring both confidentiality and integrity of the audio data. In addition, we utilize Fernet symmetric encryption and PBKDF2HMAC (Password-Based Key Derivation Function 2 with HMAC) for key generation, supported by generative hash passwords to further strengthen security. The signal energy ratio feature of audio signals is defined and used in the watermark embedding method through feature quantification, improving the resilience of the watermarking system. First, the original audio is encrypted using scrambling, multiplication, and AES-GCM to generate the encrypted data. The encrypted data is then divided into frames, each compressed through sampling. The compressed data, along with frame numbers, is embedded into the encrypted audio, forming the watermarked signal which is uploaded to third-party storage. Authorized users retrieve the encrypted data and verify its authenticity. If intact,the data is decrypted directly using Fernet to recover the original audio. In the case of an attack,the compromised frames are identified, and the embedded compressed data is used to reconstruct the audio approximately. The reconstructed signal is subsequently decrypted to retain the expression meaning of the original audio. Experimental results demonstrate the effectiveness of the proposed scheme in providing quantum-safe encryption, secure watermarking, and forensics capabilities. Keywords: Audio Encryption, Watermarking, AES-GCM, Fernet, PBKDF2HMAC, DWT, Signal Energy Ratio, Cybersecurity.
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Venkatesh, M. "A Novel Digital Audio Encryption and Watermarking Scheme." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem.ncft043.
Full textAbstract:
Abstract—To enhance the privacy and security of audio signals stored in third-party storage centers, a robust digital audio encryption and forensics watermarking scheme is proposed. The scheme incorporates the AES-GCM (Advanced Encryption Standard in Galois/Counter Mode) algorithm for authenticated encryption, ensuring both confidentiality and integrity of the audio data. In addition, we utilize Fernet symmetric encryption and PBKDF2HMAC (Password-Based Key Derivation Function 2 with HMAC) for key generation, supported by generative hash passwords to further strengthen security. The signal energy ratio feature of audio signals is defined and used in the watermark embedding method through feature quantification, improving the resilience of the watermarking system. First, the original audio is encrypted using scrambling, multiplication, and AES-GCM to generate the encrypted data. The encrypted data is then divided into frames, each compressed through sampling. The compressed data, along with frame numbers, is embedded into the encrypted audio, forming the watermarked signal which is uploaded to third-party storage. Authorized users retrieve the encrypted data and verify its authenticity. If intact,the data is decrypted directly using Fernet to recover the original audio. In the case of an attack,the compromised frames are identified, and the embedded compressed data is used to reconstruct the audio approximately. The reconstructed signal is subsequently decrypted to retain the expression meaning of the original audio. Experimental results demonstrate the effectiveness of the proposed scheme in providing quantum-safe encryption, secure watermarking, and forensics capabilities. Keywords: Audio Encryption, Watermarking, AES-GCM, Fernet, PBKDF2HMAC, DWT, Signal Energy Ratio, Cybersecurity.
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SMUSHKIN, A. B. "FORENSICS IN DIGITAL WORLDS (USING THE EXAMPLE OF ONLINE GAMES)." Vestnik Tomskogo gosudarstvennogo universiteta, no. 502 (2024): 214–19. https://doi.org/10.17223/15617793/502/22.
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The author notes the growing need to counteract crimes committed in or with the help of “digital worlds”. Digital worlds mean artificial digital simulations of reality in the form of meta-universes of various levels (from metaverses, metacampuses of universities to full-scale metaverses, such as the one developed by Meta or simulation computer realities (from simulators to online games). The need to develop measures to counter such crimes has already been realized both by foreign public authorities (USA, UAE, etc.) and by international organizations (Interpol, etc.). The article examines crimes that can be committed in meta-space (considering it a special case of the embodiment of cyberspace in a visualized (and, in the future, using other senses) way that allows users to communicate with each other and with the surrounding space through avatars). It is stated that of all the “digital worlds”, online games have become the most widespread. Therefore, the main attention in the article is paid specifically to online games, for many forensic recommendations are similar (and thus such developments can be extrapolated) for two-dimensional online games, computer-simulated realities (VR, AR, etc.), as well as for the metaspace of the metaverses. Considering separate functions of some games, the author also points to the possibility of using games for the preparation, commission and concealment of crimes, as well as criminals’ communication in these processes. An almost complete absence of forensic recommendations in the analyzed field is emphasized. The author differentiates crimes in the digital worlds of the sphere under consideration: crimes committed through online games in the real world; torts committed in the game world; crimes and other torts that can be conditionally called “near-game” that can be committed outside the game, on forums, social networks and other resources, but according to the circumstances related to the game. The author analyzes the main problems of the investigation and offers forensic recommendations aimed at solving them. The following features of the investigation of crimes in online games are emphasized and analyzed: game environment; geographical diversity of the players; connection with the real world. It is stated that the need to emphasize the issue of the forensics of digital worlds is largely caused by the digital transformation of various types of public relations The author declares no conflicts of interests.
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Sudjayanti, Shafa alya, and Dani Hamdani. "Digital Forensic Analysis Of APK Files In Phishing Scams On Whatsapp Using The NIST Method." Brilliance: Research of Artificial Intelligence 4, no. 1 (2024): 100–110. http://dx.doi.org/10.47709/brilliance.v4i1.3800.
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Cybercrime targeting Android devices through phishing methods, especially through the WhatsApp messaging app, has emerged as a critical issue in cybersecurity. It requires comprehensive investigation and analysis. This research attempts to address this critical issue by conducting a comprehensive digital forensic investigation using the National Institute of Standards and Technology (NIST) framework methodology. Using advanced reverse engineering techniques and Vscode's APKTool extension tool, called APKLab, the research carefully examines the structure and mechanism of hidden encoded APK files that aim to steal sensitive information, such as SMS messages containing one-time passwords (OTPs), and send them via the Telegram app to attackers who can use them to access personal and banking data. As a result, this research provides a deeper understanding of the cybersecurity threats to Android devices and suggestions for mitigation measures for users and organizations. The recommendations are consistent with NIST principles. They emphasize the importance of user education, application source code reviews, system updates, and considering the use of additional security software. By filling an important gap in digital forensics, this research aims to provide insight into preventing and mitigating phishing scams via APK files on WhatsApp Android. It also highlights the importance of strong cybersecurity measures and encourages continued research efforts to effectively counter emerging cyber threats.
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Mulla, A. S. "Real-Time Cyber Security Protection Tool." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 183–86. https://doi.org/10.22214/ijraset.2025.65807.
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This report aims to examine the evolving cybersecurity threats, including DeepFakes, phishing, social engineering, and malware, and analyze detection mechanisms to counter these threats. DeepFakes, generated using advanced AI techniques, pose risks such as identity theft and disinformation, with detection models like CNNs and RNNs showing promise, albeit with reduced effectiveness against high-quality manipulations. Tools like Face Forensics++ are instrumental for training such models. Phishing, which employs deceptive techniques to steal sensitive information, is addressed through URL-based detection systems and datasets such as Phish-Tank. Social engineering, leveraging tactics like pretexting and baiting, highlights the importance of NLP models for detecting manipulation in communications. Malware, encompassing threats like ransomware and spyware, continues to challenge cybersecurity efforts, with machine learning and deep learning approaches proving effective for detection. Tools like Virus Total and Cuckoo Sandbox enhance detection through multi-engine scanning and dynamic analysis. While detection technologies show significant potential, challenges such as real-time threat identification and user awareness underscore the need for integrated, adaptive cybersecurity solutions
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Kerrakchou, Imane, Adil Abou El Hassan, Sara Chadli, Mohamed Emharraf, and Mohammed Saber. "Selection of efficient machine learning algorithm on Bot-IoT dataset for intrusion detection in internet of things networks." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (2023): 1784. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1784-1793.
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With the growth of internet of things (IoT) systems, they have become the target of malicious third parties. In order to counter this issue, realistic investigation and protection countermeasures must be evolved. These countermeasures comprise network forensics and network intrusion detection systems. To this end, a well-organized and representative data set is a crucial element in training and validating the system's credibility. In spite of the existence of multiple networks, there is usually little information provided about the botnet scenarios used. This article provides the Bot-IoT dataset that embeds traces of both legitimate and simulated IoT networks as well as several types of the attacks. It provides also a realistic test environment to address the drawbacks of existing datasets, namely capturing complete network information, precise labeling, and a variety of recent and complex attacks. Finally, this work evaluates the confidence of the Bot-IoT dataset by utilizing a variety of machine learning and statistical methods. This work will provide a foundation to enable botnet identification on IoT-specific networks.
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V, Dreshpak, Prokopovych-Tkachenko D, and Rybalchenko L. "Comprehensive digital image analysis to detect manipulation." Artificial Intelligence 30, AI.2025.30(1) (2025): 77–83. https://doi.org/10.15407/jai2025.01.077.
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Comprehensive digital image analysis plays an important role in modern digital forensics and cybersecurity, as it allows detecting fakes, tampering and hidden traces of editing in photographs or other visual data. These methods can be used by OSINT (Open Source Intelligence) specialists and investigative journalists to detect fakes and counter-propaganda. This article describes a scientific and methodological approach aimed at detecting manipulations in digital images based on a combination of various algorithms and data processing technologies. The article considers contour and gradient analysis (Kenny's method), detection of editing traces through metadata analysis (EXIF), Error Level Analysis (ELA), as well as spectral and wavelet analysis. Based on a systematic review of the results of applying these methods to a sample of different types of images, it is demonstrated that comprehensive analysis has significant advantages over the use of individual methods, as it allows for the fullest possible identification of potential traces of manipulation, including copying and pasting of fragments, digital artefacts from excessive compression, and inconsistencies in the internal structures of images. The article contains a description of the methodology, including the necessary mathematical models, which allows us to generalise and formalise the analysis procedure. The results of the study confirm the high accuracy and reliability of the proposed approach. Recommendations for the practical use of complex digital image analysis in the fields of forensic science, media, cyberattack investigations and intellectual property protection are proposed, and promising areas for further research in this area are outlined
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Kruchinina, N. V. "Criminalistic Implementation of Counteraction to the Use of Biotechnologies in the event of Commission of Criminal Offences". Lex Russica, № 2 (28 лютого 2022): 101–7. http://dx.doi.org/10.17803/1729-5920.2022.183.2.101-107.
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Achievements in the field of biotechnology raise the standard of human life and improve its quality, open up new opportunities for social activity, including for business, and are also used when combating crime. The paper analyzes the problems associated with the study of trace substances of human biological origin, the collection and storage of genomic information, the formation of such a direction as DNA forensics. The author examines the issue of criminal threats associated with the use of biotechnologies, including crimes committed with the use of biological weapons, bioterrorism, and analyzes the criminal use of biotechnologies, including the sphere of assisted human reproduction. The paper defines the possibilities of criminalistics in the process of countering the use of biotechnologies in the commission of criminal offences. First, we are talking about the development of measures to prevent crimes in this area, creation of methods for investigating crimes committed in this area, development of effective technical, tactical and methodological recommendations for verifying information that is significant from a forensic standpoint.The author proves that further development of biotechnologies is impossible without proper legal regulation, without protection from criminal risks. The high level of abuse, including of a criminal nature, in the field of genomic research determines the need to form a scientific basis for developing measures to respond to criminal risks in the field of artificial human reproduction, as well as measures to counter criminal activity in this area. In view of this, according to the author, there is a need to adopt a separate federal law on assisted reproductive technologies.
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Kowalski, Marcin, and Krzysztof Mierzejewski. "Detection of 3D face masks with thermal infrared imaging and deep learning techniques." Photonics Letters of Poland 13, no. 2 (2021): 22. http://dx.doi.org/10.4302/plp.v13i2.1091.
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Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural network-featured method for detecting presentation attacks. Full Text: PDF ReferencesS.R. Arashloo, J. Kittler, W. Christmas, "Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features", IEEE Trans. Inf. Forensics Secur. 10, 11 (2015). CrossRef A. Anjos, M.M. Chakka, S. Marcel, "Motion-based counter-measures to photo attacks inface recognition", IET Biometrics 3, 3 (2014). CrossRef M. Killioǧlu, M. Taşkiran, N. Kahraman, "Anti-spoofing in face recognition with liveness detection using pupil tracking", Proc. SAMI IEEE, (2017). CrossRef A. Asaduzzaman, A. Mummidi, M.F. Mridha, F.N. Sibai, "Improving facial recognition accuracy by applying liveness monitoring technique", Proc. ICAEE IEEE, (2015). CrossRef M. Kowalski, "A Study on Presentation Attack Detection in Thermal Infrared", Sensors 20, 14 (2020). CrossRef C. Galdi, et al, "PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT - a case study", IET Biometrics 9, 6 (2020). CrossRef D.A. Socolinsky, A. Selinger, J. Neuheisel, "Face recognition with visible and thermal infrared imagery", Comput. Vis Image Underst. 91, 1-2 (2003) CrossRef L. Sun, W. Huang, M. Wu, "TIR/VIS Correlation for Liveness Detection in Face Recognition", Proc. CAIP, (2011). CrossRef J. Seo, I. Chung, "Face Liveness Detection Using Thermal Face-CNN with External Knowledge", Symmetry 2019, 11, 3 (2019). CrossRef A. George, Z. Mostaani, D Geissenbuhler, et al., "Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network", IEEE Trans. Inf. Forensics Secur. 15, (2020). CrossRef S. Ren, K. He, R. Girshick, J. Sun, "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition", Proc. CVPR IEEE 39, (2016). CrossRef K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition", Proc. CVPR, (2016). CrossRef K. Mierzejewski, M. Mazurek, "A New Framework for Assessing Similarity Measure Impact on Classification Confidence Based on Probabilistic Record Linkage Model", Procedia Manufacturing 44, 245-252 (2020). CrossRef
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Journal, IJSREM. "Deep Fake Face Detection Using Deep Learning Tech with LSTM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28624.
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The fabrication of extremely life like spoof films and pictures that are getting harder to tell apart from actual content is now possible because to the quick advancement of deep fake technology. A number of industries, including cybersecurity, politics, and journalism, are greatly impacted by the widespread use of deepfakes, which seriously jeopardizes the accuracy of digital media. In computer vision, machine learning, and digital forensics, detecting deepfakes has emerged as a crucial topic for study and development. An outline of the most recent cutting-edge methods and difficulties in deep fake detection is given in this abstract. In this article, we go over the fundamental ideas behind deepfake creation and investigate the many approaches used to spot and stop the spread of fake news. Methods include sophisticated machine learning algorithms trained on enormous datasets of real and fake media, as well as conventional forensic investigation. We explore the principal characteristics and artifacts that differentiate authentic video from deepfakes, such as disparities in audio-visual synchronization, aberrant eye movements, and inconsistent facial emotions. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), two deep learning frameworks, have been used by researchers to create sophisticated detection models that can recognize minute modifications in multimedia information. The fast developments in deep fake generating techniques, however, continue to exceed efforts in detection and mitigation, making deep fake detection a daunting problem. The issue is made worse by the democratization of deepfake technology and its accessibility to non-experts, which calls for creative solutions and multidisciplinary cooperation to counter this expanding danger. Keywords: convolutional neural network,, generative adversarial network, deep fake ,long short term memory,
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Garcia, Jan Mark. "Exploring Deepfakes and Effective Prevention Strategies: A Critical Review." Psychology and Education: A Multidisciplinary Journal 33, no. 1 (2025): 93–96. https://doi.org/10.70838/pemj.330107.
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Deepfake technology, powered by artificial intelligence and deep learning, has rapidly advanced, enabling the creation of highly realistic synthetic media. While it presents opportunities in entertainment and creative applications, deepfakes pose significant risks, including misinformation, identity fraud, and threats to privacy and national security. This study explores the evolution of deepfake technology, its implications, and current detection techniques. Existing methods for deepfake detection, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), are examined, highlighting their effectiveness and limitations. The study also reviews state-of-the-art approaches in image forensics, phoneme-viseme mismatch detection, and adversarial training to counter deepfake threats. Moreover, the ethical and legal challenges surrounding deepfakes are discussed, emphasizing the need for policy regulations and collaborative efforts between governments, tech companies, and researchers. As deepfake technology continues to evolve, so must detection strategies, integrating multimodal analysis and real-time verification systems. This research underscores the importance of developing robust detection frameworks and public awareness initiatives to mitigate the risks associated with deepfakes. Future directions include enhancing detection algorithms through explainable AI, improving dataset quality, and integrating blockchain for digital content authentication. By providing a comprehensive analysis of deepfake creation, detection, and countermeasures, this study contributes to the ongoing discourse on synthetic media and its societal impact. Addressing these challenges requires interdisciplinary collaboration and continuous innovation to safeguard digital integrity and trust in the information ecosystem.
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Barni, Mauro, Marco Fontani, and Benedetta Tondi. "A Universal Attack Against Histogram-Based Image Forensics." International Journal of Digital Crime and Forensics 5, no. 3 (2013): 35–52. http://dx.doi.org/10.4018/jdcf.2013070103.
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In this paper the authors propose a universal image counter-forensic scheme that contrasts any detector based on the analysis of the image histogram. Being universal, the scheme does not require knowledge of the detection algorithms available to the forensic analyst, and can be used to conceal traces left in the histogram of the image by any processing tool. Instead of adapting the histogram of the image to fit some statistical model, the proposed scheme makes it practically identical to the histogram of an untouched image, by solving an optimization problem. In doing this, the perceptual similarity between the processed and counter-attacked image is preserved to a large extent. The validity of the scheme in countering both contrast-enhancement and splicing- detection is assessed through experimental validation.
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Dr., Alungat Barbara (PhD). "The Current Responses by the Uganda Police Force in Enhancing Urban Peace Enforcement in Uganda; A Case of Kampala City." International Journal of Social Science And Human Research 06, no. 05 (2023): 2666–78. https://doi.org/10.5281/zenodo.7908339.
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<strong>Purporse:</strong> This study examined the urban policing paradox in Uganda with specific regard to how rapid urbanisation poses challenges for policing and urban peace enforcement in Kampala city. This study was to explore the crimes accompanying rapid urbanisation in Kampala city; examine the role of the Uganda Police Force in urban policing and peace enforcement in Kampala <strong>city;</strong> analyse the challenges of urban policing and peace enforcement in Kampala city and finally explore the current responses by the Uganda Police Force in enhancing urban peace enforcement in Kampala city. The study employed two theories:the social contract and broken windows. <strong>Methodology:</strong> Different materials and sources were used during data collection, which included documentary reviews, key informant interviews and focus group discussions. <strong>Findings:</strong> The study found out that Kampala city remains a vulnerable place to urban insecurity primarily due to an ever-increasing population, coupled with the challenges that hinder effective policing in the city. Tthe study revealed that the Uganda Police Force has made various milestones like the CCTV Cameras, forensics capabilities, 999 communication system, interagency cooeperation and community engagement in promoting peace and security in Kampala city. These are done through collaboration with other stakeholders such as development partners, security agencies, government ministries and agencies, local leaders and the community. The specialized directorates and units within the Uganda Police Force were operationalized to strengthen urban security in the wake of different security threats like terrorism. While the members of the Uganda Police Force perform their duties, they encounter challenges that affect their constitutional duties' performance. Nonetheless, the police has greatly worked towards strengthening peace and security in Kampala. This has been through improvement on the anti-crime infrastructure such as the use of CCTV cameras, finger printing of guns, counter terrorism, revival of the 999 system, among other interventions geared towards strengthening urban policing and peace enforcement in Kampala City.
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Pravin, Kumar, and Neelam. "Real-time pixel pattern analysis for deepfake detection: Unveiling eye blinking dynamics in live video streams." International Journal of Trends in Emerging Research and Development 2, no. 3 (2024): 13–16. https://doi.org/10.5281/zenodo.12516486.
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Deepfake technology poses significant threats to security, privacy, and trust in digital media. This paper introduces a novel approach to deepfake detection by analyzing pixel patterns related to eye blinking dynamics in real-time video streams. By leveraging machine learning algorithms to detect anomalies in eye blinking, our method offers an effective and efficient solution for identifying deepfake content. This study, focused on the Indian context, provides insights into the implementation challenges, accuracy, and potential applications of this technology. Thanks to advances in processing power, deep learning algorithms have made it very simple to create extremely lifelike synthetic movies, or "deep fakes." These movies provide serious threats including extortion, political manipulation, and staging phoney terrorist incidents since they can realistically switch faces. In this research, a unique deep learning approach for effectively differentiating between real movies and AI-manipulated ones is presented. The suggested approach extracts frame-level features from movies using a Res-Next Convolutional Neural Network (CNN), picking up on minute details and patterns in each frame. A recurrent neural network (RNN) built on Long Short-Term Memory (LSTM) is then trained using these characteristics. The LSTM network uses its capacity to record temporal information to evaluate the series of frames and detect whether the video has been changed. The method is extensively evaluated on a sizable, well-balanced, and diverse dataset that was produced by fusing together many pre-existing datasets, including Face Forensics++, the Deep Fake Detection Challenge, and Celeb-DF. This extensive dataset improves the model's ability to detect deep fakes in real-world settings by simulating real-time events. The system attempts to provide strong detection skills by including these datasets in a way that reflects a variety of video quality and processing approaches. The ultimate goal is to utilise AI to counter the risks that AI poses by developing a trustworthy technique for automatically identifying deepfakes. With its use of state-of-the-art deep learning algorithms, this methodology marks a major breakthrough in the battle against digital manipulation and safeguards the integrity of video footage.
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Poliakov, Igor. "Defense Versions in Modern Forensics: Concept and Types." Siberian Criminal Process and Criminalistic Readings, no. 2 (44) (July 5, 2024): 87–94. https://doi.org/10.17150/2411-6122.2024.2.87-94.
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The article considers the concept and types of defense versions as a new variety of criminalistic versions. According to the author, defense versions are relevant not only to the lawyer's practice, but also to other types of activities connected with the enforcement of laws and human rights in the criminal legal sphere. The article notes that three types of versions can be used in the forensic analysis of the circumstances of a criminal case: main versions, counter-versions and alternative versions. The main version is the accusatory version, which determines the initial direction of the investigator's actions and receives considerable attention in scientific research and publications on investigative practice. Counter-versions are versions that contradict the main version. Alternative versions are versions that do not contradict the main version, but are alternative explanations or hypotheses proposed to explain the disputed circumstances of the case. The author of the article concludes that the coexistence of these three types of versions forms a comprehensive system for investigation, allowing the investigator to avoid a one-sided accusatory approach and providing a more objective and comprehensive investigation of crimes.
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Fléron, René W. "Satellite Forensics: Analysing Sparse Beacon Data to Reveal the Fate of DTUsat-2." International Journal of Aerospace Engineering 2019 (May 5, 2019): 1–12. http://dx.doi.org/10.1155/2019/8428167.
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The CubeSat DTUsat-2 was designed and built by students and faculty at the Technical University of Denmark and launched to low earth orbit on June 2014. Its mission was to aid ornithologists in bird migration research. Shortly after launch and orbit injection, it became apparent that all was not nominal. To understand the problem and find the causes, a forensic investigation was initiated. The investigation used recorded Morse-encoded beacons emitted by the satellite as a starting point. This paper presents the real-life data from DTUsat-2 on orbit and the methodologies used to visualize the key element in the investigation, namely, the correlation between orbit position and the beacon counter. Based on the data presented, an explanation for the observed behaviour of DTUsat-2 is given.
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Hagen, Raymond André. "Computational Forensics: The Essential Role of Logs in APT and Advanced Cyberattack Response." International Conference on Cyber Warfare and Security 20, no. 1 (2025): 547–54. https://doi.org/10.34190/iccws.20.1.3328.
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Advanced Persistent Threats (APTs) represent one of the most complex challenges in modern cybersecurity, characterized by their stealth, persistence, and sophistication. This study investigates the critical yet underutilized role of log analysis in detecting and responding to APTs, drawing on semi-structured interviews with 12 cybersecurity professionals from diverse sectors. Findings highlight logs as indispensable tools for identifying anomalies, reconstructing attack timelines, and understanding adversary tactics, techniques, and procedures (TTPs). However, barriers such as overwhelming data volumes, lack of standardization, and limited analytical tools hinder their effective utilization. To address these challenges, the study proposes actionable recommendations, including the adoption of standardized log formats, AI-driven real-time analysis, enhanced visibility across systems, and collaboration for threat intelligence sharing. These findings underscore logs’ dual role as investigative assets and catalysts for improved cybersecurity resilience, offering a strategic roadmap for leveraging log analysis to counter evolving APT threats.
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Bezerra, Islan Amorim, Rubens Karman Paula da Silva, Sidney Marlon Lopes De Lima, Sergio Murilo Maciel Fernandes, Carolina de Lira Matos, and Jheklos Gomes Da Silva. "DEJAVU FORENSICS: ENHANCING RECOVERY OF FORMATTED JPEG AND PNG DATA USING SUPPORT VECTOR MACHINES." ARACÊ 7, no. 3 (2025): 15398–434. https://doi.org/10.56238/arev7n3-301.
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With technological advancements, virtual crimes are occurring more frequently. When digital equipment is stolen, lost, or discarded, the data remains stored on the disks, enabling its recovery. This work focuses on the recovery of formatted files, investigating the applicability of the tools Foremost, Scalpel, and Magic Rescue in Linux, as well as an in-house tool equipped with machine learning. The goal is to develop a tool for the recovery and validation of formatted files, contributing to investigations of digital crimes and bringing new insights into recovery methods. Using pattern recognition, the cluster is used as input, acting as a neuron in the learning machine. The work applies machine learning to recognize patterns in blocks/clusters. In the "simple" scenario, the classification is binary (class vs. counter class), a methodology developed by Pavel (2017). In the "complex" scenario, the one-against-all method was used, with a database of 16,000 files. The research presents an approach that combines machine learning and data science to recover formatted data. The in-house tool achieves a recovery rate of over 96% for formatted PNG and JPEG files, running in seconds.
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Qureshi, Shavez Mushtaq, Atif Saeed, Sultan H. Almotiri, Farooq Ahmad, and Mohammed A. Al Ghamdi. "Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media." PeerJ Computer Science 10 (May 27, 2024): e2037. http://dx.doi.org/10.7717/peerj-cs.2037.
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The rapid advancement of deepfake technology poses an escalating threat of misinformation and fraud enabled by manipulated media. Despite the risks, a comprehensive understanding of deepfake detection techniques has not materialized. This research tackles this knowledge gap by providing an up-to-date systematic survey of the digital forensic methods used to detect deepfakes. A rigorous methodology is followed, consolidating findings from recent publications on deepfake detection innovation. Prevalent datasets that underpin new techniques are analyzed. The effectiveness and limitations of established and emerging detection approaches across modalities including image, video, text and audio are evaluated. Insights into real-world performance are shared through case studies of high-profile deepfake incidents. Current research limitations around aspects like cross-modality detection are highlighted to inform future work. This timely survey furnishes researchers, practitioners and policymakers with a holistic overview of the state-of-the-art in deepfake detection. It concludes that continuous innovation is imperative to counter the rapidly evolving technological landscape enabling deepfakes.
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Stefanov, D. S. "DETECTION AND PREVENTION OF CRIMES IN HIGHER EDUCATIONAL INSTITUTIONS." Herald of criminal justice, no. 3-4 (2023): 332–47. https://doi.org/10.17721/2413-5372.2023.3-4/332-347.
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This article is dedicated to a comprehensive analysis of the issue of crime in higher education institutions (HEIs), which poses a significant threat to the safety of the educational environment, academic integrity, and public trust in the education system. The purpose of the article is to investigate the problems of detecting and preventing crimes in HEIs and to develop proposals for their resolution. The main types of offenses most frequently encountered in HEIs are identified, including corruption crimes, violence, cybercrimes, illicit drug trafficking, and property crimes. Particular attention is paid to the specifics of each of these crimes, their manifestations, and consequences for students, faculty, and staff of educational institutions. The article analyzes the legal mechanisms for preventing and combating crime in HEIs, specifically examining Ukraine’s national legislation regulating safety issues in the field of education. The role of normative legal acts, including the Law of Ukraine «On Higher Education,» the Law of Ukraine «On Prevention of Corruption,» the Criminal Code of Ukraine, as well as government decrees governing the work of law enforcement agencies in combating offenses in education, is explored. The importance of developing and implementing internal anti-corruption policies in HEIs, introducing systems for monitoring compliance with academic integrity, and increasing the legal awareness of students and faculty is emphasized. The role of law enforcement agencies in detecting and documenting crimes in HEIs is examined. Special attention is given to the activities of the National Police of Ukraine, the Bureau of Economic Security, and the Security Service of Ukraine, which conduct operational and investigative activities, inspect financial operations of HEIs for illegal schemes, and counter threats to national security in the field of education. Mechanisms of cooperation between law enforcement agencies and university administrations, as well as methods of prompt response to potential threats, are described. A separate section of the article is devoted to the use of modern technologies for monitoring and preventing crimes. The possibilities of using video surveillance systems, audio recording, analytical tools, and digital forensics methods to improve the effectiveness of detecting offenses are explored. The role of big data and artificial intelligence in developing risk profiles and predicting potential crimes on HEI premises is considered. An analysis of international experience in combating crime in higher education institutions is conducted. Particular attention is paid to the American Clery Act, which establishes requirements for mandatory reporting of crimes on university campuses, transparency of information about offenses, and safety measures in HEIs. The possibility of adapting the principles of the Clery Act in Ukrainian legislation is explored, including the mandatory publication of crime rate reports in HEIs, the introduction of standard incident response protocols, and the expansion of functions of internal security services in educational institutions. The author proposes a comprehensive approach to detecting and preventing crimes in HEIs, combining the analysis of legal mechanisms, the activities of law enforcement agencies, and modern technological solutions. The importance of interagency cooperation between educational institutions, law enforcement structures, and state bodies to ensure the safety of the educational environment is emphasized.
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Seema, Bagchi (Chattaraj), Chakrabortty Ashutosh, K. Kuila D., and Chandra Lahiri Sujit. "Comparison of the composition profiles of the low explosives in India from forensic exhibits and a brief discussion on preventive forensic techniques." Journal of Indian Chemical Society 93, Jul 2016 (2016): 889–906. https://doi.org/10.5281/zenodo.5639449.
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Central Forensic Science Laboratory, Government of India, 30, Gorachand Road, Kolkata-700 014, India E-mail : bagchiseema@gmail.com, sujitclahiri@yahoo.com Fax : 91-33-22849442 Geological Survey of India, Government of India, Kolkata-700 069, India <em>E-mail</em> : cashu@rediffmail.com Chemical Examination Laboratory, Excise Department, Government of West Bengal, Kolkata-700 046, India <em>E-mail</em> : kuiladk@rediffmail.com More than 1650 pre blast and post blast exhibits were analyzed using routine analytical procedures supplemented by advanced analytical techniques like Ion-Chromatography (IC) (mainly), Scanning Electron Microscope with Energy Dispersive X-ray Analyzer (SEM-EDXA), Fourier Transforms Infrared Spectroscopy (FTIR), Atomic Absorption Spectrometer (AAS) (for a number of samples) for inorganic constituents and High Performance Liquid Chromatography (HPLC), High Performance Thin Layer Chromatography (HPTLC), Gas Chromatograph-Mass Spectrometer (GC-MS), UV-Visible Spectrophotometer, Fourier Transform Infrared Spectroscope (FTIR), Liquid Chromatography-Tandem Mass Spetrometry (LC/MS/MS) for organic constituents. Results of the investigations suggested that high explosives like nitroglycerine, di and tri nitrotoluene (DNT and TNT), tetryl, cyclonite (RDX), pentaerythritol tetranitrate (PETN) were rarely used except in mortars and detonators. Common easily available unrestricted chemicals like potassium nitrate/ chlorate (KNO<sub>3</sub>/KClO<sub>3</sub>), arsenic sulphide (As<sub>2</sub>S<sub>3</sub>), sulphur, aluminium powder of different mesh size and sodium, calcium, magnesium, barium, strontium (Na<sup>+</sup>, Ca<sup>+</sup>, Mg<sup>+</sup>, Ba<sup>+</sup>, Sr<sup>+</sup>) nitrate with varying compositions along with splinters were used. But there were perceptible changes in the modus operandi of the terrorists. There had been a spurt in the use of different types of ammonium nitrate (AN) based explosives like AN, AN+Al, ANFO (ammonium nitrate and fuel oil/ diesel/kerosene), AN+wax, AN based gel/emulsion/slurry explosives with other ingredients. Urea nitrate was also obtained. The article contains a brief description of works on AN+wax, AN based emulsion and uranium nitrate based explosives sent to CFSL and examined in the laboratory of CFSL. Aspects relating to ascertain the trace of the origin of the explosives by determining the ‘isotopic signature’ of the elements (C, N, O, H) in the explosives and biomarker fingerprinting of the petroleum products in the explosives were discussed. Figures relating to the quantitative estimation of the compounds of the explosives and some experimental figures related to the validation of the experimental findings have been incorporated. However, in view of increased terrorist activities and development of new arsenals, it is desirable to make on spot examination of explosive residues using high sensitive explosive detection system (EDS) and explosive trace detection (ETD) techniques. Restriction and proper monitoring of the explosive materials and the desirability of undertaking counter terrorism or preventive forensic protocols to limit terrorist activities are needed.
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Emre, Caglar Hosgor. "Detection and Mitigation of Anti-Forensics." December 31, 2020. https://doi.org/10.5281/zenodo.4425258.
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Abstract—With the advances in IT, digital forensics became an important part of the juridical system. On the other hand, cybercriminals have been developing counter tactics against digital forensics for fleeing from the justice. Those tactics are grouped under the term “anti-forensics”. Anti-forensics includes data hiding, artifact wiping and trail obfuscation techniques which aim to subvert, hinder or make dysfunctional the digital forensic analysis. There are more than 300 anti-forensics related tools and methods. Categorization of, detection the use and mitigation against anti-forensics’ related resources do improve digital forensic analysis processes. Therefore, this research aims to provide categorization of anti-forensics techniques by explaining how cyber-criminals use the tools and also aims to provide counter methods or mitigation techniques. Keywords-component; computer forensics; anti-forensics