A novel adversarial example generation algorithm based on WGAN-Unet

Tian Yao; Jiarong Fan; Zhongyuan Qin; Liquan Chen

doi:10.1117/12.2681589

1 June 2023 A novel adversarial example generation algorithm based on WGAN-Unet

Tian Yao, Jiarong Fan, Zhongyuan Qin, Liquan Chen

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 1271817 (2023) https://doi.org/10.1117/12.2681589
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

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.

Citation Download Citation

Tian Yao, Jiarong Fan, Zhongyuan Qin, and Liquan Chen "A novel adversarial example generation algorithm based on WGAN-Unet", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 1271817 (1 June 2023); https://doi.org/10.1117/12.2681589

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