Comments (2)
Contributions in that direction are welcome.
Some aspects are already possible with Foolbox, e.g. attacking non-differentiable models using our decision-based attacks and enforcing discrete inputs (by adding a rounding layer before the actual input).
Attacking only a subset of the input is also already possible by modifying the model such that it get's a constant input (that's part of the model) and a modifiable input that will be fed to the attack. It might not be the most convenient way of doing this, but it should work well.
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Closing this because there are no plans to work on this and it's somewhat unclear what exactly would be needed.
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Related Issues (20)
- Example Code Running Failed HOT 1
- [tests/test_models] The results of `transform_bounds` are inconsistent between CPU and GPU. HOT 3
- Are there any plans to support attacks on TFLite models? HOT 1
- Changing CUDA device at runtime HOT 1
- Logit optimization
- about PGD attack HOT 2
- specifying criterion fails with TypeError HOT 2
- "nll_loss_forward_no_reduce_cuda_kernel_index" not implemented for 'Float' HOT 3
- Deprecation warning using old scipy namespace for gaussian_filter
- how to define the bounds HOT 2
- About the pgd attacks HOT 1
- how to use GaussianBlurAttack HOT 1
- FGSM TargetedMisclassfication HOT 1
- Use foolbox for multi-label classification HOT 1
- Local datasets supported?
- Is there a criterion for query budget? HOT 1
- It seems like the 'success' value in the return of the 'attack' function is overconfident. HOT 2
- About Carlini-Wagner Attack
- Are the wrong classified images sorted out? HOT 1
- It seems your CI/CD has a bug. HOT 1
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