Scale-free Photo-realistic Adversarial Pattern Attack
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Traditional pixel-wise image attack algorithms suffer from poor robustness to defense algorithms, i.e., the attack strength degrades dramatically when defense algorithms are applied. Although Generative Adversarial Networks (GAN) can partially address this problem by synthesizing a more semantically meaningful texture pattern, the main limitation is that existing generators can only generate images of a specific scale. In this paper, we propose a scale-free generation-based attack algorithm that synthesizes semantically meaningful adversarial patterns globally to images with arbitrary scales. Our generative attack approach consistently outperforms the state-of-the-art methods on a wide range of attack settings, i.e. the proposed approach largely degraded the performance of various image classification, object detection, and instance segmentation algorithms under different advanced defense methods.
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