# Generating Adversarial yet Inconspicuous Patches with a Single Image

arXiv: https://arxiv.org/abs/2009.09774 · HTML: https://mlsanity.com/paper/2009.09774/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Benchmarking Adversarial Patch Selection and Location](https://mlsanity.com/paper/2508.01676/) (2025)

## Abstract (arXiv)

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.