# Category-wise Attack: Transferable Adversarial Examples for Anchor Free Object Detection

arXiv: https://arxiv.org/abs/2003.04367 · HTML: https://mlsanity.com/paper/2003.04367/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection](https://mlsanity.com/paper/2201.08970/) (2022)
- [Object Detectors in the Open Environment: Challenges, Solutions, and Outlook](https://mlsanity.com/paper/2403.16271/) (2024)
- [A Review of Adversarial Attacks in Computer Vision](https://mlsanity.com/paper/2308.07673/) (2023)
- [SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors?](https://mlsanity.com/paper/2206.10809/) (2022)
- [Object-fabrication Targeted Attack for Object Detection](https://mlsanity.com/paper/2212.06431/) (2022)

## Abstract (arXiv)

Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbations can completely change the classification results. Their vulnerability has led to a surge of research in this direction. However, most works dedicated to attacking anchor-based object detection models. In this work, we aim to present an effective and efficient algorithm to generate adversarial examples to attack anchor-free object models based on two approaches. First, we conduct category-wise instead of instance-wise attacks on the object detectors. Second, we leverage the high-level semantic information to generate the adversarial examples. Surprisingly, the generated adversarial examples it not only able to effectively attack the targeted anchor-free object detector but also to be transferred to attack other object detectors, even anchor-based detectors such as Faster R-CNN.