Black-box attacks on face recognition via affine-invariant training

Deep neural network (DNN)-based face recognition has shown impressive performance in verification; however, recent studies reveal a vulnerability in deep face recognition algorithms, making them susceptible to adversarial attacks. Specifically, these attacks can be executed in a black-box manner with limited knowledge about the target network. While this characteristic is practically significant due to hidden model details in reality, it presents challenges such as high query budgets and low success rates. To improve the performance of attacks, we establish the whole framework through affine-invariant training, serving as a substitute for inefficient sampling. We also propose AI-block-a novel module that enhances transferability by introducing generalized priors. Generalization is achieved by creating priors with stable features when sampled over affine transformations. These priors guide attacks, improving efficiency and performance in black-box scenarios. The conversion via AI-block enables the transfer gradients of a surrogate model to be used as effective priors for estimating the gradients of a black-box model. Our method leverages this enhanced transferability to boost both transfer-based and query-based attacks. Extensive experiments conducted on 5 commonly utilized databases and 7 widely employed face recognition models demonstrate a significant improvement of up to 11.9 percentage points in success rates while maintaining comparable or even reduced query times.