Comments (1)
Good day!
The gradient-based attacks only supports end-to-end differentiable networks, since gradient is mapped to each byte of the input model. By shadow classifier, do you mean a differentiable surrogate of the target? If yes, if such surrogate is end-to-end differentiable or partially, you can still use the given code (provided that is also using an embedding).
In previous versions of secml, that trick was needed to instantiate an attack (since the surrogate model was needed).
If you improve the library, please consider to push a pull request, it would be awesome!
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Related Issues (20)
- How to run lightGBM and SOREL model using secml_malware? HOT 2
- No data preprocessing for SorelNet? HOT 2
- Error while running the sample attack code from blackbox_tutorial.ipynb HOT 4
- real sample generation HOT 5
- can't attack EMBER model HOT 1
- Confidence on Microsoft Malware Classification Challenge HOT 10
- Differences Between WhiteBox Attacks HOT 7
- Adding support for QuoVadis models HOT 2
- AttributeError: 'NoneType' object has no attribute 'dos_header' HOT 4
- No such file or directory: 'secml_malware/data/malware_samples/test_folder' HOT 3
- lightGBM and SOREL model weights? HOT 1
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- CGammaSectionsEvasionProblem attack budget HOT 6
- FGSM Attacking Running for days HOT 1
- Train models HOT 5
- Fix numpy retrocompatibility for CClassifierEmber
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