The security of deep neural networks has triggered extensive research on the adversarial example. The gradient or optimization-based adversarial example generation algorithm has poor practicality and cannot combine high success rate and high efficiency; using GAN to generate adversarial examples has the problems of gradient disappearance and unstable generation. In this paper, we propose a novel adversarial example generation algorithm based on WGAN-Unet, which uses the structure of WGAN and Unet to form a generative adversarial network to improve the stability of network training, and uses the cosine loss function to measure the category loss and improve the success rate of adversarial attacks. The adversarial example generation using WGAN-Unet is compared with other algorithms in terms of human eye perception, time consumption, attack success rate, and image quality, proving our scheme’s superiority.
The Editor-in-Chief and the publisher have retracted this article, which was submitted as part of a guest-edited special section. An investigation uncovered evidence of systematic manipulation of the publication process, including compromised peer review. The Editor and publisher no longer have confidence in the results and conclusions of the article.LS did not agree with the retraction. YM, YL, and LC either did not respond directly or could not be reached.
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