Fine-grained Synthesis of Unrestricted Adversarial Examples
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We propose a novel approach for generating unrestricted adversarial examples by manipulating fine-grained aspects of image generation. Unlike existing unrestricted attacks that typically hand-craft geometric transformations, we learn stylistic and stochastic modifications leveraging state-of-the-art generative models. This allows us to manipulate an image in a controlled, fine-grained manner without being bounded by a norm threshold. Our model can be used for both targeted and non-targeted unrestricted attacks. We demonstrate that our attacks can bypass certified defenses, yet our adversarial images look indistinguishable from natural images as verified by human evaluation. Adversarial training can be used as an effective defense without degrading performance of the model on clean images. We perform experiments on LSUN and CelebA-HQ as high resolution datasets to validate efficacy of our proposed approach.
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