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Improving Transferability of Adversarial Patches on Face Recognition with Generative Models by Xiao, Zihao; Gao, Xianfeng; Fu, Chilin; Dong, Yinpeng; Gao, Wei; Zhang, Xiaolu; Zhou, Jun; Zhu, Jun is a scholarly article available to read on EtoBox.

What is Improving Transferability of Adversarial Patches on Face Recognition with Generative Models about?

Face recognition is greatly improved by deep convolutional neural networks (CNNs). Recently, these face recognition models have been used for identity authentication in security sensitive applications. However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stealthy, raising new security concerns on the real-world applications of these models. In this paper, we evaluate the robustness of face recognition models using adversarial patches based on transferability, where the attacker has limited accessibility to the target models. First, we extend the existing transfer-based attack techniques to generate transferable adversarial patches. However, we observe that the transferability is sensitive to initialization and degrades when the perturbation magnitude is large, indicating the overfitting to the substitute models. Second, we propose to regularize the adversarial patches on the low dimensional data manifold. The manifold is represented by generative models pre-trained on legitimate human face images. Using face-like features as adversarial perturbations through optimization on the manifold, we show that the gaps between the responses of substit

Author

Xiao, Zihao; Gao, Xianfeng; Fu, Chilin; Dong, Yinpeng; Gao, Wei; Zhang, Xiaolu; Zhou, Jun; Zhu, Jun

Published

2021

Language

EN