Existing adversarial attacks against Object Detectors (ODs) suffer from two inherent limitations. Firstly, ODs have complicated meta-structure designs, hence most advanced attacks for ODs concentrate on attacking specific detector-intrinsic structures, which makes it hard for them to work on other detectors and motivates us to design a generic attack against ODs. Secondly, most works against ODs make Adversarial Examples (AEs) by generalizing image-level attacks from classification to detection, which brings redundant computations and perturbations in semantically meaningless areas (\eg, backgrounds) and leads to an emergency for seeking controllable attacks for ODs. To this end, we propose a generic white-box attack, LGP (local perturbations with adaptively global attacks), to blind mainstream object detectors with controllable perturbations. For a detector-agnostic attack, LGP tracks high-quality proposals and optimizes three heterogeneous losses simultaneously. In this way, we can fool the crucial components of ODs with a part of their outputs without the limitations of specific structures. Regarding controllability, we establish an object-wise constraint that exploits foreground-background separation adaptively to induce the attachment of perturbations to foregrounds. Experimentally, the proposed LGP successfully attacked sixteen state-of-the-art object detectors on MS-COCO and DOTA datasets, with promising imperceptibility and transferability obtained.
Please refer to install.md for installation guide.
Please see MMdet_get_started.md for the basic usage of MMdet.
Please see MMrotate_get_started.md for the basic usage of MMrotate.
Please refer to COCO.md to prepare the COCO data.
Please refer to Dota.md to prepare the DOTA data.
More runnings can be seen in train.md
CUDA_VISIBLE_DEVICES=2 python mmdet/adv/test/test_adv.py mmdet/adv/configs/faster_rcnn_adv/faster_rcnn_r50_fpn_1x_coco_adv.py mmdet/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth --eval bbox --work-dir ./work-dir --show-dir ./images
You should first modify line 52 in data root to the adversarial examples dir.
CUDA_VISIBLE_DEVICES=2 python mmdet/tools/test.py mmdet/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py mmdet/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth --eval bbox --work-dir ./work-dir --show-dir ./images
MMdetection, and MMRotate are open source projects that are contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
If you find our work useful, please consider citing our paper:
@misc{2307.12342,
Author = {Guopeng Li and Yue Xu and Jian Ding and Gui-Song Xia},
Title = {Towards Generic and Controllable Attacks Against Object Detection},
url = {https://arxiv.org/abs/2307.12342}
Year = {2023},
Eprint = {arXiv:2307.12342},
}
Please feel free to contact us if you have any problems.
Email: [email protected]