A Survey of Machine Learning Techniques in Adversarial Image Forensics

• URL: http://arxiv.org/abs/2010.09680v1
• Date: Mon, 19 Oct 2020 17:16:38 GMT
• Title: A Survey of Machine Learning Techniques in Adversarial Image Forensics
• Authors: Ehsan Nowroozi, Ali Dehghantanha, Reza M. Parizi, Kim-Kwang Raymond Choo
• Abstract summary: Image forensic plays a crucial role in both criminal investigations and civil litigation. Machine learning approaches are also utilized in image forensics. This paper surveys techniques that can be used to enhance the robustness of machine learning-based binary manipulation detectors.
• Score: 45.219116050446786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image forensic plays a crucial role in both criminal investigations (e.g., dissemination of fake images to spread racial hate or false narratives about specific ethnicity groups) and civil litigation (e.g., defamation). Increasingly, machine learning approaches are also utilized in image forensics. However, there are also a number of limitations and vulnerabilities associated with machine learning-based approaches, for example how to detect adversarial (image) examples, with real-world consequences (e.g., inadmissible evidence, or wrongful conviction). Therefore, with a focus on image forensics, this paper surveys techniques that can be used to enhance the robustness of machine learning-based binary manipulation detectors in various adversarial scenarios.

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