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\noindent Traditional L_p norm-restricted image attack algorithms suffer frompoor transferability to black box scenarios and poor robustness to defensealgorithms. Recent CNN generator-based attack approaches can synthesizeunrestricted and semantically meaningful entities to the image, which is shownto be transferable and robust. However, such methods attack images by eithersynthesizing local adversarial entities, which are only suitable for attackingspecific contents or performing global attacks, which are only applicable to aspecific image scale. In this paper, we propose a novel Patch QuiltingGenerative Adversarial Networks (PQ-GAN) to learn the first scale-free CNNgenerator that can be applied to attack images with arbitrary scales forvarious computer vision tasks. The principal investigation on transferabilityof the generated adversarial examples, robustness to defense frameworks, andvisual quality assessment show that the proposed PQG-based attack frameworkoutperforms the other nine state-of-the-art adversarial attack approaches whenattacking the neural networks trained on two standard evaluation datasets(i.e., ImageNet and CityScapes).

Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator