Comments (4)
self._process_gradient simply backprops the gradient through the preprocessing step (subtraction of the mean, scaling).
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This line is for backprops.
foolbox/foolbox/models/pytorch.py
Line 98 in 021f02a
The point it is why do we need:
foolbox/foolbox/models/base.py
Line 75 in 021f02a
Based on:
foolbox/foolbox/attacks/gradient.py
Line 30 in 021f02a
The estimated grad is applied to the original image. Why do we need to do the preprocessing on the grad then?
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See, the processing pipeline is this: original image -> preprocessed image -> model output. Most importantly, the model only sees the preprocessed image! Hence, loss.backward() gives you the gradient with respect to the preprocessed image. self._process_gradient takes this gradient and returns the gradient with respect to the original image. If there is no preprocessing then the two are identical.
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@wielandbrendel I got it! Thanks!
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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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