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Workshop: A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning

Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE

Wenzhao Xiang · Chang Liu · Shibao Zheng


Traditional adversarial examples are typically generated by adding perturbation noise to the input image within a small matrix norm. In practice, unrestricted adversarial attack has raised great concern and presented a new threat to the AI safety. In this paper, we propose a wavelet-VAE structure to reconstruct an input image and generate adversarial examples by modifying the latent code. Different from perturbation-based attack, the modifications of the proposed method are not limited but imperceptible to human eyes. Experiments show that our method can generate high quality adversarial examples on ImageNet dataset.

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