Adversarial Sample Optimization in Multi-Domains

Adversarial attacks have attracted much attention in the field of vision. In recent years, many scholars have proposed their own views in the field of adversarial attacks. Traditional methods generate adversarial samples with very small differences in the spatial domain compared to the original samples, but in fact, there is still a large difference when transferring the adversarial samples to the frequency domain. In order to improve the ability of adversarial attacks and reduce the amount of frequency domain disturbance, this paper proposes a cross-domain optimization algorithm for adversarial samples. The main idea is to crop the perturbation range of the adversarial samples generated by adversarial attacks and the original images in the frequency domain space, and combine the optimized samples after cropping with the original samples to form new adversarial samples. Experiments show that this method eliminates the differences generated by the adversarial samples in the frequency domain space while hardly reducing the aggressiveness of the adversarial samples. It further improves the stealth of the adversarial samples with almost no burden.