Comments (1)
ryderling
commented on July 17, 2024
Fixed in d4e1181 in defining the model for both MNIST and CIFAR10, though it is suggested by PyTorch officially (https://github.com/pytorch/examples/blob/master/mnist/main.py).
Nothing needs to be changed in our implementation of BIM and PGD.
After retraining the model for MNIST and attacking, the misclassification rates of BIM and PGD at eps=0.3 on MNIST are both 100%.
from deepsec.
Related Issues (16)
- Attacks are not run on defenses in an all-pairs manner HOT 2
- Reporting success rate of unbounded attacks is meaningless HOT 2
- Paper does not report attack success rate for targeted adversarial examples HOT 2
- Discrepancies between tables, text, and code HOT 7
- Significant and fundamental flaws in methodology, analysis, and conclusions HOT 1
- JSMA implementation is incorrect
- What's the difference between UMIFGSM and TMIFGSM HOT 2
- Paper uses averages instead of the minimum for security analysis HOT 2
- FGSM implementation is incorrect HOT 9
- PGD adversarial training implementation is incorrect HOT 3
- Computing the average over different threat models is meaningless HOT 2
- Comparing attack effectiveness is done incorrectly HOT 2
- Epsilon values studied are too large to be meaningful HOT 4
- Detection defenses set per-attack thresholds HOT 2
- Attack success rate decreases with distortion bound HOT 2
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