Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result. The vulnerability has led to a surge of research in this direction, including adversarial attacks on object detection networks. However, previous studies are dedicated to attacking anchor-based object detectors. In this paper, we present the first adversarial attack on anchor-free object detectors. It conducts category-wise, instead of previously instance-wise, attacks on object detectors, and leverages high-level semantic information to efficiently generate transferable adversarial examples, which can also be transferred to attack other object detectors, even anchor-based detectors such as Faster R-CNN. Experimental results on two benchmark datasets demonstrate that our proposed method achieves state-of-the-art performance and transferability.
@article{arxiv.2106.01618,
title = {Transferable Adversarial Examples for Anchor Free Object Detection},
author = {Quanyu Liao and Xin Wang and Bin Kong and Siwei Lyu and Bin Zhu and Youbing Yin and Qi Song and Xi Wu},
journal= {arXiv preprint arXiv:2106.01618},
year = {2021}
}