# Sparse Adversarial Attack to Object Detection

arXiv: https://arxiv.org/abs/2012.13692 · HTML: https://mlsanity.com/paper/2012.13692/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [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)
- [SoK: Anti-Facial Recognition Technology](https://mlsanity.com/paper/2112.04558/) (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)
- [To Make Yourself Invisible with Adversarial Semantic Contours](https://mlsanity.com/paper/2303.00284/) (2023)
- [Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers](https://mlsanity.com/paper/2111.08954/) (2021)
- [SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors?](https://mlsanity.com/paper/2206.10809/) (2022)
- [Patch is Enough: Naturalistic Adversarial Patch against Vision-Language Pre-training Models](https://mlsanity.com/paper/2410.04884/) (2024)

## Abstract (arXiv)

Adversarial examples have gained tons of attention in recent years. Many adversarial attacks have been proposed to attack image classifiers, but few work shift attention to object detectors. In this paper, we propose Sparse Adversarial Attack (SAA) which enables adversaries to perform effective evasion attack on detectors with bounded emphl0 norm perturbation. We select the fragile position of the image and designed evasion loss function for the task. Experiment results on YOLOv4 and FasterRCNN reveal the effectiveness of our method. In addition, our SAA shows great transferability across different detectors in the black-box attack setting. Codes are available at https://github.com/THUrssq/Tianchi04.