Reverse Engineering of Deceptions on Machine- and Human-Centric Attacks
Yuguang Yao, Xiao Guo, Vishal Asnani, Yifan Gong, Jiancheng Liu, Xue Lin, Xiaoming Liu, Sijia Liu · Foundations and Trends® in Privacy and Security · 2024
This work presents a comprehensive exploration of Reverse Engineering of Deceptions (RED) in the field of adversarial machine learning. It delves into the intricacies of machine- and human-centric attacks, providing a holistic understanding of how adversarial strategies can be reverse-engineered to safeguard AI systems. For machine-centric attacks, we cover reverse engineering methods for pixel-level perturbations, adversarial saliency maps, and victim model information in adversarial examples. In the realm of human-centric attacks, the focus shifts to generative model information inference and manipulation localization from generated images. Through this work, we offer a forward-looking perspective on the challenges and opportunities associated with RED. In addition, we provide foundational and practical insights in the realms of AI security and trustworthy computer vision.