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Traditional adversarial examples are typically generated by addingperturbation noise to the input image within a small matrix norm. In practice,un-restricted adversarial attack has raised great concern and presented a newthreat to the AI safety. In this paper, we propose a wavelet-VAE structure toreconstruct an input image and generate adversarial examples by modifying thelatent code. Different from perturbation-based attack, the modifications of theproposed method are not limited but imperceptible to human eyes. Experimentsshow that our method can generate high quality adversarial examples on ImageNetdataset.

Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE