AI Evasion and Impersonation Attacks on Facial Re-Identification with Activation Map Explanations
Researchers developed a novel framework for generating adversarial patches that can fool facial recognition systems through both evasion and impersonation attacks. The method reduces facial recognition accuracy from 90% to 0.4% in white-box settings and demonstrates strong cross-model generalization, highlighting critical vulnerabilities in surveillance systems.