Physical Adversarial Attacks on AI Surveillance Systems:Detection, Tracking, and Visible--Infrared Evasion

This research paper examines physical adversarial attacks on AI surveillance systems through a surveillance-oriented lens, emphasizing that robustness cannot be assessed from isolated image benchmarks alone. The study highlights critical gaps in current evaluation practices, including temporal persistence across frames, multi-modal sensing (visible and infrared), realistic attack carriers, and system-level objectives that must be tested under actual deployment constraints.