PPOM-Attack: A Substitute Model-Free Perturbation Prediction and Optimization Method for Black-Box Adversarial Attack Against Face Recognition

Summary

PPOM-Attack is a black-box attack against face recognition that avoids substitute models by taking feedback directly from the target model. It uses a proximal policy optimization (PPO)-based agent to predict and disturb perturbation regions in a face image, and a minimum brightness offsets method to keep adversarial images high quality. The authors report a 21.7% average gain in attack success rate over state-of-the-art FR attacks across seven FR models.

Topics

• Adversarial machine learning

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