The blooming of social media and face recognition (FR) systems has increased
people's concern about privacy and security. A new type of adversarial privacy
cloak (class-universal) can be applied to all the images of regular users, to
prevent malicious FR systems from acquiring their identity information. In this
work, we discover the optimization dilemma in the existing methods -- the local
optima problem in large-batch optimization and the gradient information
elimination problem in small-batch optimization. To solve these problems, we
propose Gradient Accumulation (GA) to aggregate multiple small-batch gradients
into a one-step iterative gradient to enhance the gradient stability and reduce
the usage of quantization operations. Experiments show that our proposed method
achieves high performance on the Privacy-Commons dataset against black-box face
recognition models.

社交媒体和人脸识别系统的普及增加了人们对隐私和安全的关注。我们提出了一种新型对抗性隐私披风技术，该技术适用于正常用户的所有图像，以防止恶意人脸识别系统获取其身份信息。我们还发现了现有方法中的优化困境，并提出了渐变累积（GA）来解决这些问题。实验证明，我们的方法在隐私 - 公用数据集上对黑盒人脸识别模型具有高性能。