Comments (4)
jonasrauber
commented on May 24, 2024
Can you explain why you think that this is problematic?
We are to some extent free to define these attacks... e.g. LBFGSAttack := GradientAttack + LBFGS
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wielandbrendel
commented on May 24, 2024
Sure, but the comparison is then screwed and bugs in LBFGS are harder to find (if you screw up the LBFGS implementation you will not get a failure but you will simply get the result of the GradientAttack). Right now LBFGS cannot get worse then GradientAttack, but it turns out that LBFGS and the SaliencyMapAttack actually perform worse then GradientAttack for many samples. This is something we want to report in the Benchmark.
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jonasrauber
commented on May 24, 2024
Agreed
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jonasrauber
commented on May 24, 2024
@wielandbrendel can this be closed?
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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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