Find in Library

The existing generator-based adversarial example generation model can effectively reduce the construction time of an adversarial example compared to the algorithms based on iterative original image modification, but the obvious differences between generated adversarial example and original image are noticeable in human perception. This model aims to increase the similarity between the adversarial example and the original image in human per-ception, while maintaining the fooling ratio. The model considers adversarial example generation process as image enhancement to the original image, introduces generative adversarial network, and improves perceptual loss func-tion to increase the similarity between adversarial example and original image in content and latent space. It also uses multi-classifier loss function to train the generator so that it can improve attack efficiency. The experimental results show that compared with other generator-based models, this model effectively improves the structural simi-larity index between the adversarial example and the original one, and the fooling ratio does not decrease. It shows that while maintaining the fooling ratio, this model can effectively improve the similarity between adversarial exa-mple and original image in human perception.