Comments (1)
Thank you for your interest in our paper.
Yes. For simple datasets like cifar10, mnist, and gtsrb, the warping mask plays an important role in creating a powerful backdoor attack. For example, the warping field should be concentrated (have high values) on the region of the object (i.e. the center of the image).
However, for now, we just use random warping masks. Therefore, the backdoor effects would be unstable against backdoor detection methods like NC.
There are two tricks that might increase the stability of backdoor effects: 1. Increasing warping strength
from warping-based_backdoor_attack-release.
Related Issues (12)
- Questions about Neural Cleanse
- Doubt HOT 1
- About cross-ratio and input_cross in the code HOT 2
- Issues about attack privileges HOT 2
- Does the wraping method apply to images whose height is not equal to its width? HOT 1
- Questions regarding reproducing results using Neural Cleanse HOT 1
- Some question about fine-pruning-celeba.py HOT 2
- About finetune defense HOT 1
- Some remaining questions with fine-pruning-celeba.py
- about the grid_temps HOT 1
- all2all-attack HOT 1
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from warping-based_backdoor_attack-release.