A Survey of Machine Learning Techniques in Adversarial Image Forensics
It addresses the problem of adversarial attacks on image forensic systems, which is critical for legal and investigative applications, but as a survey, it is incremental in nature.
This paper surveys machine learning techniques to improve the robustness of binary manipulation detectors in adversarial image forensics, addressing vulnerabilities that can lead to real-world consequences like inadmissible evidence or wrongful convictions.
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.