# Minority Reports Defense: Defending Against Adversarial Patches

arXiv: https://arxiv.org/abs/2004.13799 · HTML: https://mlsanity.com/paper/2004.13799/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 30 of 36)

- [Certifiably Robust RAG against Retrieval Corruption](https://mlsanity.com/paper/2405.15556/) (2024)
- [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)
- [Detecting Backdoors in Pre-trained Encoders](https://mlsanity.com/paper/2303.15180/) (2023)
- [Harnessing Perceptual Adversarial Patches for Crowd Counting](https://mlsanity.com/paper/2109.07986/) (2021)
- [Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift](https://mlsanity.com/paper/2312.00050/) (2023)
- [DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks](https://mlsanity.com/paper/2102.02956/) (2021)
- [PatchGuard++: Efficient Provable Attack Detection against Adversarial Patches](https://mlsanity.com/paper/2104.12609/) (2021)
- [ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking](https://mlsanity.com/paper/2202.01811/) (2022)
- [REAP: A Large-Scale Realistic Adversarial Patch Benchmark](https://mlsanity.com/paper/2212.05680/) (2022)
- [Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches](https://mlsanity.com/paper/2103.08860/) (2021)
- [Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey](https://mlsanity.com/paper/2206.08304/) (2022)
- [Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation](https://mlsanity.com/paper/2209.05980/) (2022)
- [ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers](https://mlsanity.com/paper/2110.14120/) (2021)
- [BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense](https://mlsanity.com/paper/2301.06241/) (2023)
- [Jujutsu: A Two-stage Defense against Adversarial Patch Attacks on Deep Neural Networks](https://mlsanity.com/paper/2108.05075/) (2021)
- [Backdoor Vulnerabilities in Normally Trained Deep Learning Models](https://mlsanity.com/paper/2211.15929/) (2022)
- [LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning](https://mlsanity.com/paper/2403.17188/) (2024)
- [PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testing](https://mlsanity.com/paper/2111.10481/) (2021)
- [Visual Adversarial Attacks and Defenses in the Physical World: A Survey](https://mlsanity.com/paper/2211.01671/) (2022)
- [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)
- [Defending Person Detection Against Adversarial Patch Attack by using Universal Defensive Frame](https://mlsanity.com/paper/2204.13004/) (2022)
- [Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow](https://mlsanity.com/paper/2310.17403/) (2023)
- [DECK: Model Hardening for Defending Pervasive Backdoors](https://mlsanity.com/paper/2206.09272/) (2022)
- [Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks](https://mlsanity.com/paper/2208.09195/) (2022)
- [PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification](https://mlsanity.com/paper/2308.11822/) (2023)
- [CrossCert: A Cross-Checking Detection Approach to Patch Robustness Certification for Deep Learning Models](https://mlsanity.com/paper/2405.07668/) (2024)
- [Agnostic Multi-Robust Learning Using ERM](https://mlsanity.com/paper/2303.08944/) (2023)
- [A Majority Invariant Approach to Patch Robustness Certification for Deep Learning Models](https://mlsanity.com/paper/2308.00452/) (2023)

## Abstract (arXiv)

Deep learning image classification is vulnerable to adversarial attack, even if the attacker changes just a small patch of the image. We propose a defense against patch attacks based on partially occluding the image around each candidate patch location, so that a few occlusions each completely hide the patch. We demonstrate on CIFAR-10, Fashion MNIST, and MNIST that our defense provides certified security against patch attacks of a certain size.