  Deep neural networks have been shown vulnerable toadversarial patches, where
exotic patterns can resultin models wrong prediction. Nevertheless, existing
ap-proaches to adversarial patch generation hardly con-sider the contextual
consistency between patches andthe image background, causing such patches to be
eas-ily detected and adversarial attacks to fail. On the otherhand, these
methods require a large amount of data fortraining, which is computationally
expensive. To over-come these challenges, we propose an approach to gen-erate
adversarial yet inconspicuous patches with onesingle image. In our approach,
adversarial patches areproduced in a coarse-to-fine way with multiple scalesof
generators and discriminators. Contextual informa-tion is encoded during the
Min-Max training to makepatches consistent with surroundings. The selection
ofpatch location is based on the perceptual sensitivity ofvictim models.
Through extensive experiments, our ap-proach shows strong attacking ability in
both the white-box and black-box setting. Experiments on saliency de-tection
and user evaluation indicate that our adversar-ial patches can evade human
observations, demonstratethe inconspicuousness of our approach. Lastly, we
showthat our approach preserves the attack ability in thephysical world.
