# Object Hider: Adversarial Patch Attack Against Object Detectors

arXiv: https://arxiv.org/abs/2010.14974 · HTML: https://mlsanity.com/paper/2010.14974/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 23 of 23)

- [Adversarial Sticker: A Stealthy Attack Method in the Physical World](https://mlsanity.com/paper/2104.06728/) (2021)
- [Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection](https://mlsanity.com/paper/2112.04532/) (2021)
- [T-SEA: Transfer-based Self-Ensemble Attack on Object Detection](https://mlsanity.com/paper/2211.09773/) (2022)
- [Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?](https://mlsanity.com/paper/2203.08392/) (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)
- [Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook](https://mlsanity.com/paper/2308.06173/) (2023)
- [RPATTACK: Refined Patch Attack on General Object Detectors](https://mlsanity.com/paper/2103.12469/) (2021)
- [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)
- [IPatch: A Remote Adversarial Patch](https://mlsanity.com/paper/2105.00113/) (2021)
- [Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?](https://mlsanity.com/paper/2108.07229/) (2021)
- [Attacking Object Detector Using A Universal Targeted Label-Switch Patch](https://mlsanity.com/paper/2211.08859/) (2022)
- [An Effective and Robust Detector for Logo Detection](https://mlsanity.com/paper/2108.00422/) (2021)
- [Enhancing Transferability of Adversarial Examples with Spatial Momentum](https://mlsanity.com/paper/2203.13479/) (2022)
- [Multifaceted Evaluation of Audio-Visual Capability for MLLMs: Effectiveness, Efficiency, Generalizability and Robustness](https://mlsanity.com/paper/2504.16936/) (2025)
- [SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors?](https://mlsanity.com/paper/2206.10809/) (2022)
- [Defending Adversarial Patches via Joint Region Localizing and Inpainting](https://mlsanity.com/paper/2307.14242/) (2023)
- [A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges](https://mlsanity.com/paper/2503.00384/) (2025)
- [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)

## Abstract (arXiv)

Deep neural networks have been widely used in many computer vision tasks. However, it is proved that they are susceptible to small, imperceptible perturbations added to the input. Inputs with elaborately designed perturbations that can fool deep learning models are called adversarial examples, and they have drawn great concerns about the safety of deep neural networks. Object detection algorithms are designed to locate and classify objects in images or videos and they are the core of many computer vision tasks, which have great research value and wide applications. In this paper, we focus on adversarial attack on some state-of-the-art object detection models. As a practical alternative, we use adversarial patches for the attack. Two adversarial patch generation algorithms have been proposed: the heatmap-based algorithm and the consensus-based algorithm. The experiment results have shown that the proposed methods are highly effective, transferable and generic. Additionally, we have applied the proposed methods to competition "Adversarial Challenge on Object Detection" that is organized by Alibaba on the Tianchi platform and won top 7 in 1701 teams. Code is available at: https://github.com/FenHua/DetDak