# Fast Local Attack: Generating Local Adversarial Examples for Object Detectors

arXiv: https://arxiv.org/abs/2010.14291 · HTML: https://mlsanity.com/paper/2010.14291/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [A Survey on Physical Adversarial Attack in Computer Vision](https://mlsanity.com/paper/2209.14262/) (2022)
- [Consistent Semantic Attacks on Optical Flow](https://mlsanity.com/paper/2111.08485/) (2021)
- [Attacking Important Pixels for Anchor-free Detectors](https://mlsanity.com/paper/2301.11457/) (2023)

## Abstract (arXiv)

The deep neural network is vulnerable to adversarial examples. Adding imperceptible adversarial perturbations to images is enough to make them fail. Most existing research focuses on attacking image classifiers or anchor-based object detectors, but they generate globally perturbation on the whole image, which is unnecessary. In our work, we leverage higher-level semantic information to generate high aggressive local perturbations for anchor-free object detectors. As a result, it is less computationally intensive and achieves a higher black-box attack as well as transferring attack performance. The adversarial examples generated by our method are not only capable of attacking anchor-free object detectors, but also able to be transferred to attack anchor-based object detector.