Relevant bibliographies by topics / Adversarial multimedia forensics

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Adversarial multimedia forensics'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Adversarial multimedia forensics"

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.., Ossama, and Mhmed Algrnaodi. "Deep Learning Fusion for Attack Detection in Internet of Things Communications." Fusion: Practice and Applications 9, no. 2 (2022): 27–47. http://dx.doi.org/10.54216/fpa.090203.

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The increasing deep learning techniques used in multimedia and networkIoT solve many problems and increase performance. Securing the deep learning models, multimedia, and networkIoT has become a major area of research in the past few years which is considered to be a challenge during generative adversarial attacks over the multimedia or networkIoT. Many efforts and studies try to provide intelligent forensics techniques to solve security issues. This paper introduces a holistic organization of intelligent multimedia forensics that involve deep learning fusion, multimedia, and networkIoT forens
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Zou, Hao, Pengpeng Yang, Rongrong Ni, and Yao Zhao. "Anti-Forensics of Image Contrast Enhancement Based on Generative Adversarial Network." Security and Communication Networks 2021 (March 24, 2021): 1–8. http://dx.doi.org/10.1155/2021/6663486.

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In the multimedia forensics community, anti-forensics of contrast enhancement (CE) in digital images is an important topic to understand the vulnerability of the corresponding CE forensic method. Some traditional CE anti-forensic methods have demonstrated their effective forging ability to erase forensic fingerprints of the contrast-enhanced image in histogram and even gray level cooccurrence matrix (GLCM), while they ignore the problem that their ways of pixel value changes can expose them in the pixel domain. In this paper, we focus on the study of CE anti-forensics based on Generative Adver
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Wildan, Jameel Hadi, Malallah Kadhem Suhad, and Rodhan Abbas Ayad. "A survey of deepfakes in terms of deep learning and multimedia forensics." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4408–14. https://doi.org/10.11591/ijece.v12i4.pp4408-4414.

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Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfake detection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a d
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Naik, Deepak. "Fake Media Forensics:AI – Driven Forensic Analysis of Fake Multimedia Content." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47208.

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Abstract—With the rapid advancement of deep learning techniques, the generation of synthetic media—commonly Research and development on deepfakes technology have reached new levels of sophistication. Digital security along with misinformation face serious threats because of these sophisticated methods. and privacy. Existing deepfake detection models primarily the detection methods primarily analyze either video or audio or image-based forgeries yet they seldom employ unified multi-modal examination methods. The authors introduce here a multi-modal deepfake detection system. The proposed framew
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Amerini, Irene, Mauro Barni, Sebastiano Battiato, et al. "Deepfake Media Forensics: Status and Future Challenges." Journal of Imaging 11, no. 3 (2025): 73. https://doi.org/10.3390/jimaging11030073.

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The rise of AI-generated synthetic media, or deepfakes, has introduced unprecedented opportunities and challenges across various fields, including entertainment, cybersecurity, and digital communication. Using advanced frameworks such as Generative Adversarial Networks (GANs) and Diffusion Models (DMs), deepfakes are capable of producing highly realistic yet fabricated content, while these advancements enable creative and innovative applications, they also pose severe ethical, social, and security risks due to their potential misuse. The proliferation of deepfakes has triggered phenomena like
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Maier, Anatol, and Christian Riess. "Reliable Out-of-Distribution Recognition of Synthetic Images." Journal of Imaging 10, no. 5 (2024): 110. http://dx.doi.org/10.3390/jimaging10050110.

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Generative adversarial networks (GANs) and diffusion models (DMs) have revolutionized the creation of synthetically generated but realistic-looking images. Distinguishing such generated images from real camera captures is one of the key tasks in current multimedia forensics research. One particular challenge is the generalization to unseen generators or post-processing. This can be viewed as an issue of handling out-of-distribution inputs. Forensic detectors can be hardened by the extensive augmentation of the training data or specifically tailored networks. Nevertheless, such precautions only
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Muhammad S. Mandisha, Mohamed A. Hussien, Amr K. Shalaby, and Omar M. Fahmy. "Wavelet Transform-based Methods for Forensic Analysis of Digital Images." Journal of Advanced Research in Applied Sciences and Engineering Technology 44, no. 1 (2024): 46–54. http://dx.doi.org/10.37934/araset.44.1.4654.

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In recent years, Generative Adversarial Networks (GANs) have been utilized in many applications of our daily lives to create digital media that was previously impossible. This paper utilizes GANs in multimedia forensics, where the precision and accuracy of image classification are crucial. The objective of the study is to detect sophisticated manipulated images generated by advanced deep learning methods using classical transformation methods. The method combines the classical wavelet transform for feature extraction with a classifier model to distinguish between real and fake images. The prop
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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 dee
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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 mani
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Pankhuri, Tripathi, Singh Shikha, Nishad Anubhav, and Siddiqui Farheen. "Fraudshield – Deepfake Detection Tools." International Journal of Engineering and Management Research 15, no. 2 (2025): 47–51. https://doi.org/10.5281/zenodo.15314707.

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FraudShield is a web application designed to detect and mitigate the impact of deepfakes, ensuring content authenticity and integrity. With the rise of image manipulation and deepfake videos, detecting fraudulent activities has become increasingly critical. This project introduces a hybrid detection system that integrates Convolutional Neural Networks (CNNs) to identify morphed images and manipulated content. The framework leverages machine learning techniques to detect tampered facial features, artifacts, and inconsistencies in deepfake videos and images. The CNN component analyzes visual fea
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Dissertations / Theses on the topic "Adversarial multimedia forensics"

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Nowroozi, Ehsan. "Machine Learning Techniques for Image Forensics in Adversarial Setting." Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1096177.

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The use of machine-learning for multimedia forensics is gaining more and more consensus, especially due to the amazing possibilities offered by modern machine learning techniques. By exploiting deep learning tools, new approaches have been proposed whose performance remarkably exceed those achieved by state-of-the-art methods based on standard machine-learning and model-based techniques. However, the inherent vulnerability and fragility of machine learning architectures pose new serious security threats, hindering the use of these tools in security-oriented applications, and, among them, multi
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Books on the topic "Adversarial multimedia forensics"

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Nowroozi, Ehsan, Kassem Kallas, and Alireza Jolfaei, eds. Adversarial Multimedia Forensics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49803-9.

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Nowroozi, Ehsan, and Alireza Jolfaei. Adversarial Multimedia Forensics. Springer, 2024.

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Book chapters on the topic "Adversarial multimedia forensics"

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Barni, Mauro, Wenjie Li, Benedetta Tondi, and Bowen Zhang. "Adversarial Examples in Image Forensics." In Multimedia Forensics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_16.

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AbstractWe describe the threats posed by adversarial examples in an image forensic context, highlighting the differences and similarities with respect to other application domains. Particular attention is paid to study the transferability of adversarial examples from a source to a target network and to the creation of attacks suitable to be applied in the physical domain. We also describe some possible countermeasures against adversarial examples and discuss their effectiveness. All the concepts described in the chapter are exemplified with results obtained in some selected image forensics sce
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Stamm, Matthew C., and Xinwei Zhao. "Anti-Forensic Attacks Using Generative Adversarial Networks." In Multimedia Forensics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_17.

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AbstractThe rise of deep learning has led to rapid advances in multimedia forensics. Algorithms based on deep neural networks are able to automatically learn forensic traces, detect complex forgeries, and localize falsified content with increasingly greater accuracy. At the same time, deep learning has expanded the capabilities of anti-forensic attackers. New anti-forensic attacks have emerged, including those discussed in Chap. 10.1007/978-981-16-7621-5_14 based on adversarial examples, and those based on generative adversarial networks (GANs). In this chapter, we discuss the emerging threat
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Nagavi, Trisiladevi C., P. Mahesha, and S. G. Kruthika. "Domain Specific Information Based Learning for Facial Image Forensics." In Adversarial Multimedia Forensics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49803-9_6.

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Nabavirazavi, Seyedsina, Rahim Taheri, Mani Ghahremani, and Sundararaja Sitharama Iyengar. "Model Poisoning Attack Against Federated Learning with Adaptive Aggregation." In Adversarial Multimedia Forensics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49803-9_1.

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Veksler, Maryna, and Kemal Akkaya. "Good or Evil: Generative Adversarial Networks in Digital Forensics." In Adversarial Multimedia Forensics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49803-9_3.

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Sun, Chengzhe, Ehab AlBadawy, Timothy F. Davison, Sarah R. Robinson, Ming-Ching Chang, and Siwei Lyu. "Using Vocoder Artifacts For Audio Deepfakes Detection." In Adversarial Multimedia Forensics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49803-9_11.

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Wu, Hanzhou, Tianyu Yang, Xiaoyan Zheng, and Yurun Fang. "Linguistic Steganography and Linguistic Steganalysis." In Adversarial Multimedia Forensics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49803-9_7.

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Schmitt, Lars, and Gökhan Kul. "Anti Forensic Measures and Their Impact on Forensic Investigations." In Adversarial Multimedia Forensics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49803-9_10.

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Ghelichkhani, Samaneh, Yousef Ahmed Mohammed Al-Dhameri Salem, Huseyn Salahov, Feras Ashor Ibrik Adam, Ahmad Jad Charbatji, and Marwa Issam Abdulkareem. "Generative Adversarial Networks for Artificial Satellite Image Creation and Manipulation." In Adversarial Multimedia Forensics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49803-9_5.

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Fang, Shengbang, and Matthew C. Stamm. "Refined GAN-Based Attack Against Image Splicing Detection and Localization Algorithms." In Adversarial Multimedia Forensics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49803-9_4.

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Conference papers on the topic "Adversarial multimedia forensics"

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Barni, Mauro, Matthew C. Stamm, and Benedetta Tondi. "Adversarial Multimedia Forensics: Overview and Challenges Ahead." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8553305.

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Barni, Mauro, and Benedetta Tondi. "Threat Models and Games for Adversarial Multimedia Forensics." In ICMR '17: International Conference on Multimedia Retrieval. ACM, 2017. http://dx.doi.org/10.1145/3078897.3080533.

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Wang, Run, Ziheng Huang, Zhikai Chen, Li Liu, Jing Chen, and Lina Wang. "Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/107.

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DeepFake is becoming a real risk to society and brings potential threats to both individual privacy and political security due to the DeepFaked multimedia are realistic and convincing. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and failed in blocking the disinformation spreading in advance. To address this limitation, researchers study the proactive defense techniques by adding adversarial noises into the source data to disrupt the DeepFake manipulation. However, the existing studies on proactive DeepFake defense via injecting adversarial noises are
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Khatoon, Shadma, and Mohammad Sarosh Umar. "Forensic sketch-to-photo transformation with improved Generative Adversarial Network (GAN)." In 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT). IEEE, 2022. http://dx.doi.org/10.1109/impact55510.2022.10029068.

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