  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.
