论文标题
faceguard：针对对抗脸部图像的自制防御
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images
论文作者
论文摘要
针对对抗性面部图像的主要防御机制往往过于对训练集中的对抗性扰动过度，而无法概括地看不见对抗性攻击。我们提出了一个新的自我监督的对抗防御框架，即面对面，可以自动检测，本地化和净化各种各样的对抗性面孔，而无需使用预先计算的对抗训练样本。在训练过程中，FaceGuard自动综合了具有挑战性和不同的对抗性攻击，使分类器能够学会区分它们与真实面孔，并试图消除图像空间中的对抗性扰动。 LFW数据集的实验结果表明，面对面可以在六种看不见的对抗攻击类型上实现99.81％的检测准确性。此外，所提出的方法可以提高Arcface的面部识别性能从34.27％的tar @ 0.1％远至无防御，降至77.46％的tar @ 0.1％。
Prevailing defense mechanisms against adversarial face images tend to overfit to the adversarial perturbations in the training set and fail to generalize to unseen adversarial attacks. We propose a new self-supervised adversarial defense framework, namely FaceGuard, that can automatically detect, localize, and purify a wide variety of adversarial faces without utilizing pre-computed adversarial training samples. During training, FaceGuard automatically synthesizes challenging and diverse adversarial attacks, enabling a classifier to learn to distinguish them from real faces and a purifier attempts to remove the adversarial perturbations in the image space. Experimental results on LFW dataset show that FaceGuard can achieve 99.81% detection accuracy on six unseen adversarial attack types. In addition, the proposed method can enhance the face recognition performance of ArcFace from 34.27% TAR @ 0.1% FAR under no defense to 77.46% TAR @ 0.1% FAR.
