# DPAttack: Diffused Patch Attacks against Universal Object Detection

arXiv: https://arxiv.org/abs/2010.11679 · HTML: https://mlsanity.com/paper/2010.11679/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 18 of 18)

- [T-SEA: Transfer-based Self-Ensemble Attack on Object Detection](https://mlsanity.com/paper/2211.09773/) (2022)
- [Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving](https://mlsanity.com/paper/2303.17297/) (2023)
- [Effectiveness Assessment of Recent Large Vision-Language Models](https://mlsanity.com/paper/2403.04306/) (2024)
- [A Survey on Physical Adversarial Attack in Computer Vision](https://mlsanity.com/paper/2209.14262/) (2022)
- [RPATTACK: Refined Patch Attack on General Object Detectors](https://mlsanity.com/paper/2103.12469/) (2021)
- [Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey](https://mlsanity.com/paper/2206.08304/) (2022)
- [Object Detectors in the Open Environment: Challenges, Solutions, and Outlook](https://mlsanity.com/paper/2403.16271/) (2024)
- [Environmental Matching Attack Against Unmanned Aerial Vehicles Object Detection](https://mlsanity.com/paper/2405.07595/) (2024)
- [Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2302.06060/) (2023)
- [Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?](https://mlsanity.com/paper/2108.07229/) (2021)
- [A Survey and Evaluation of Adversarial Attacks for Object Detection](https://mlsanity.com/paper/2408.01934/) (2024)
- [Adversarial Semantic Contour for Object Detection](https://mlsanity.com/paper/2109.15009/) (2021)
- [A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking](https://mlsanity.com/paper/2306.12111/) (2023)
- [AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models](https://mlsanity.com/paper/2409.07002/) (2024)
- [To Make Yourself Invisible with Adversarial Semantic Contours](https://mlsanity.com/paper/2303.00284/) (2023)
- [Adversarial Examples in Environment Perception for Automated Driving (Review)](https://mlsanity.com/paper/2504.08414/) (2025)
- [Patch is Enough: Naturalistic Adversarial Patch against Vision-Language Pre-training Models](https://mlsanity.com/paper/2410.04884/) (2024)
- [The Unseen Adversaries: Robust and Generalized Defense Against Adversarial Patches](https://mlsanity.com/paper/2604.26317/) (2026)

## Abstract (arXiv)

Recently, deep neural networks (DNNs) have been widely and successfully used in Object Detection, e.g. Faster RCNN, YOLO, CenterNet. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Adversarial attacks against object detection can be divided into two categories, whole-pixel attacks and patch attacks. While these attacks add perturbations to a large number of pixels in images, we proposed a diffused patch attack (DPAttack) to successfully fool object detectors by diffused patches of asteroid-shaped or grid-shape, which only change a small number of pixels. Experiments show that our DPAttack can successfully fool most object detectors with diffused patches and we get the second place in the Alibaba Tianchi competition: Alibaba-Tsinghua Adversarial Challenge on Object Detection. Our code can be obtained from https://github.com/Wu-Shudeng/DPAttack.