Comments (3)
You're right. It is impractical to make attacks on captured photos directly. But the motivation of our paper is to examine the robustness of ReID systems and to engage the community in designing a more robust ReID system, which is crucial for the applications with security-sensitive purposes. The real-world attack (like https://www.cs.cmu.edu/~sbhagava/papers/face-rec-ccs16.pdf) is indeed an important and potential direction of our work, but a good (transferable/controllable/inconspicuous) generation of adversarial perturbation is always the first task :).
As for other image retrieval tasks, we did perform an attack on Baidu APIs for Human Detection and Attributes but did not conduct experiments on Oxford building or CUB2011 datasets. But we believe that the algorithm is suitable for any problem which can be considered as / converted to a ranking problem.
from adversarial-attack-on-person-reid-with-deep-mis-ranking.
Thank you for your reply. I'll have a try with it on other image retrieval tasks.
from adversarial-attack-on-person-reid-with-deep-mis-ranking.
OK. I will temporarily close the issue. Feel free to re-open it whenever something big happens :).
from adversarial-attack-on-person-reid-with-deep-mis-ranking.
Related Issues (17)
- There are some bugs in your code. HOT 1
- 对抗样本的可视化问题 HOT 4
- Black box attack HOT 1
- eval_freq?
- 运行时出错,我是直接下载的完整代码,但是在运行时出现如下错误: HOT 4
- Would D leak knowledge to G HOT 3
- Error while running with CUHK03 dataset
- The weight files HOT 1
- G model
- utils.py应该取消import cv2 HOT 1
- alignedreid.py 缩进没有调整好 HOT 1
- 论文中的mask问题 HOT 3
- Some question about the mask HOT 3
- Feature Extraction HOT 1
- We are doing defense based on your code, but we don't know exactly how to customize our model HOT 2
- ???
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from adversarial-attack-on-person-reid-with-deep-mis-ranking.