Blackbox transfer attacks for image classifiers have been extensively studied in recent years. In contrast, little progress has been made on transfer attacks for object detectors. Object detectors take a holistic view of the image and the detection of one object (or lack thereof) often depends on other objects in the scene. This makes such detectors inherently context-aware and adversarial attacks in this space are more challenging than those targeting image classifiers. In this paper, we present a new approach to generate context-aware attacks for object detectors. We show that by using co-occurrence of objects and their relative locations and sizes as context information, we can successfully generate targeted mis-categorization attacks that achieve higher transfer success rates on blackbox object detectors than the state-of-the-art. We test our approach on a variety of object detectors with images from PASCAL VOC and MS COCO datasets and demonstrate up to 20 percentage points improvement in performance compared to the other state-of-the-art methods.
@article{arxiv.2112.03223,
title = {Context-Aware Transfer Attacks for Object Detection},
author = {Zikui Cai and Xinxin Xie and Shasha Li and Mingjun Yin and Chengyu Song and Srikanth V. Krishnamurthy and Amit K. Roy-Chowdhury and M. Salman Asif},
journal= {arXiv preprint arXiv:2112.03223},
year = {2021}
}