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.
@article{arxiv.2003.04367,
title = {Category-wise Attack: Transferable Adversarial Examples for Anchor Free Object Detection},
author = {Quanyu Liao and Xin Wang and Bin Kong and Siwei Lyu and Youbing Yin and Qi Song and Xi Wu},
journal= {arXiv preprint arXiv:2003.04367},
year = {2020}
}