A survey of machine learning techniques in adversarial image forensics
Department
Software Engineering and Game Development
Document Type
Article
Publication Date
1-1-2021
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 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.
Journal Title
Computers and Security
Journal ISSN
01674048
Volume
100
Digital Object Identifier (DOI)
10.1016/j.cose.2020.102092