  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 substitute models and the target
models dramatically decrease, exhibiting a better transferability. Extensive
digital world experiments are conducted to demonstrate the superiority of the
proposed method in the black-box setting. We apply the proposed method in the
physical world as well.
