A survey of machine learning techniques in adversarial image forensics

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 or political campaigns) 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 (e.g., how to detect adversarial (image) examples), and there are associated 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. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The paper discusses the importance of image forensics in criminal investigations and civil litigation, as well as the increasing use of machine learning in this field. It also examines the limitations, vulnerabilities, and real-world consequences associated with machine learning-based approaches, and surveys techniques to enhance the robustness of these methods in various adversarial scenarios.