Comments (4)
There are certain modules in trojanzoo workflow: dataset, model, mark, attack, defense.
Since you have already got a poisoned model, please import it in model
module.
- If it's an existing model architecture (e.g., resnet18), you only need to import the weight parameters using
model.load()
(You may need to change layer names to get adapted to trojanzoo style.) - If not, you need to register your own model class (one
_ImageModel
and anotherImageModel
, see how resnet works.)
Afterwards, you could use all existing defenses with setting attack as --attack badnet
(it's okay that your attack is actually not badnet). You need to provide mark image or tensor to mark
module to make sure the ASR validation is correct.
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Okay, I think I make it work for my use case. However, I cannot see the anomaly score for my model when using Neural Cleanse. Is there anything similar in trojanzoo?
Thanks in advance
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MAD is the anomaly score.
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If you have no question, plz close this issue.
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Related Issues (20)
- BackdoorAttack class has no argument for source_class HOT 1
- Low effective loading in get_class_subset function HOT 1
- Install newest version fail HOT 1
- RuntimeError: Dataset not found or corrupted. You can use download=True to download it HOT 10
- Clean label attack accuracy is wrong HOT 5
- In new push model path is not working HOT 1
- badnet folder information HOT 1
- [Error] When I test Neural Cleanse i got a error HOT 2
- Is it possible to apply methods to graph? HOT 6
- Input aware dynamic backdoor error HOT 5
- trojanvision.datasets.ImageFolder HOT 1
- Possible bug: target_class not changed when computing ASR for reversed triggers HOT 2
- problem about saving the intermediate results and config problem HOT 6
- strange mark saved HOT 2
- Hyperparameters for training Resnet18 on CIFAR10? HOT 1
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- Attack saving and loading is not working HOT 2
- Comp version of networks HOT 2
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