# PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch

arXiv: https://arxiv.org/abs/2207.01795 · HTML: https://mlsanity.com/paper/2207.01795/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 49 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [Square Attack: a query-efficient black-box adversarial attack via random search](https://mlsanity.com/paper/1912.00049/) (2019)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples](https://mlsanity.com/paper/1710.10766/) (2017)
- [Adversarial Logit Pairing](https://mlsanity.com/paper/1803.06373/) (2018)
- [A study of the effect of JPG compression on adversarial images](https://mlsanity.com/paper/1608.00853/) (2016)
- [DPatch: An Adversarial Patch Attack on Object Detectors](https://mlsanity.com/paper/1806.02299/) (2018)
- [LaVAN: Localized and Visible Adversarial Noise](https://mlsanity.com/paper/1801.02608/) (2018)
- [Certified Defenses for Adversarial Patches](https://mlsanity.com/paper/2003.06693/) (2020)
- [(De)Randomized Smoothing for Certifiable Defense against Patch Attacks](https://mlsanity.com/paper/2002.10733/) (2020)
- [On Physical Adversarial Patches for Object Detection](https://mlsanity.com/paper/1906.11897/) (2019)
- [Local Gradients Smoothing: Defense against localized adversarial attacks](https://mlsanity.com/paper/1807.01216/) (2018)
- [Universal Adversarial Training](https://mlsanity.com/paper/1811.11304/) (2018)
- [Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection](https://mlsanity.com/paper/2112.04532/) (2021)
- [PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier](https://mlsanity.com/paper/2108.09135/) (2021)
- [Adversarial Training against Location-Optimized Adversarial Patches](https://mlsanity.com/paper/2005.02313/) (2020)
- [Detection as Regression: Certified Object Detection by Median Smoothing](https://mlsanity.com/paper/2007.03730/) (2020)

## Built on by (most cited first, 30 of 30)

- [PAD: Patch-Agnostic Defense against Adversarial Patch Attacks](https://mlsanity.com/paper/2404.16452/) (2024)
- [PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses](https://mlsanity.com/paper/2310.13076/) (2023)
- [Adversarial Examples in the Physical World: A Survey](https://mlsanity.com/paper/2311.01473/) (2023)
- [Safety of Embodied Navigation: A Survey](https://mlsanity.com/paper/2508.05855/) (2025)
- [Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches](https://mlsanity.com/paper/2404.00540/) (2024)
- [Breaking the Illusion: Real-world Challenges for Adversarial Patches in Object Detection](https://mlsanity.com/paper/2410.19863/) (2024)
- [PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification](https://mlsanity.com/paper/2308.11822/) (2023)
- [Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications](https://mlsanity.com/paper/2311.11191/) (2023)
- [Query-Efficient Video Adversarial Attack with Stylized Logo on Service Computing](https://mlsanity.com/paper/2408.12099/) (2024)
- [PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection](https://mlsanity.com/paper/2506.23581/) (2025)
- [Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors](https://mlsanity.com/paper/2406.15104/) (2024)
- [X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail](https://mlsanity.com/paper/2306.08422/) (2023)
- [Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems](https://mlsanity.com/paper/2307.08939/) (2023)
- [Adversarial Examples are Misaligned in Diffusion Model Manifolds](https://mlsanity.com/paper/2401.06637/) (2024)
- [How to beat a Bayesian adversary](https://mlsanity.com/paper/2407.08678/) (2024)
- [A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges](https://mlsanity.com/paper/2503.00384/) (2025)
- [GAN-Based Single-Stage Defense for Traffic Sign Classification Under Adversarial Patch](https://mlsanity.com/paper/2503.12567/) (2025)
- [IAP: Invisible Adversarial Patch Attack through Perceptibility-Aware Localization and Perturbation Optimization](https://mlsanity.com/paper/2507.06856/) (2025)
- [Reinforced Embodied Active Defense: Exploiting Adaptive Interaction for Robust Visual Perception in Adversarial 3D Environments](https://mlsanity.com/paper/2507.18484/) (2025)
- [Concept-Based Masking: A Patch-Agnostic Defense Against Adversarial Patch Attacks](https://mlsanity.com/paper/2510.04245/) (2025)
- [A Single Set of Adversarial Clothes Breaks Multiple Defense Methods in the Physical World](https://mlsanity.com/paper/2510.17322/) (2025)
- [Universal Multi-view Black-box Attack against Object Detectors via Layout Optimization](https://mlsanity.com/paper/2407.06688/) (2024)
- [RED: Robust Environmental Design](https://mlsanity.com/paper/2411.17026/) (2024)
- [SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models](https://mlsanity.com/paper/2505.16318/) (2025)
- [SpaNN: Detecting Multiple Adversarial Patches on CNNs by Spanning Saliency Thresholds](https://mlsanity.com/paper/2506.18591/) (2025)
- [Defending against Patch-Based and Texture-Based Adversarial Attacks with Spectral Decomposition](https://mlsanity.com/paper/2604.10715/) (2026)
- [The Unseen Adversaries: Robust and Generalized Defense Against Adversarial Patches](https://mlsanity.com/paper/2604.26317/) (2026)
- [Detectors Learn the Wrong Thing: Shortcut-Resistant Adversarial Training Against Physically Realizable Attacks](https://mlsanity.com/paper/2607.21243/) (2026)
- [AdROD: HyperNetwork-based Adversarially Robust Object Detection for Autonomous Driving](https://mlsanity.com/paper/2608.16031/) (2026)
- [CertVLA: Certified Defense against Physical Visual Attacks for Vision-Language-Action Models](https://mlsanity.com/paper/2608.20791/) (2026)

## Abstract (arXiv)

Adversarial patch attacks mislead neural networks by injecting adversarial pixels within a local region. Patch attacks can be highly effective in a variety of tasks and physically realizable via attachment (e.g. a sticker) to the real-world objects. Despite the diversity in attack patterns, adversarial patches tend to be highly textured and different in appearance from natural images. We exploit this property and present PatchZero, a general defense pipeline against white-box adversarial patches without retraining the downstream classifier or detector. Specifically, our defense detects adversaries at the pixel-level and "zeros out" the patch region by repainting with mean pixel values. We further design a two-stage adversarial training scheme to defend against the stronger adaptive attacks. PatchZero achieves SOTA defense performance on the image classification (ImageNet, RESISC45), object detection (PASCAL VOC), and video classification (UCF101) tasks with little degradation in benign performance. In addition, PatchZero transfers to different patch shapes and attack types.