  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.
