Comments (4)
Hi mzweilin, is it possible to use only the defense part, and so without specifying any attacks?
Like with this example:
python3 main.py --dataset_name ImageNet --model_name MobileNet --nb_examples 10 --balance_sampling --detection "FeaturesSqueezing?squeezers=bit_depth_5,median_filter_2_2,non_local_means_color_11_3_4&distance_measure=l1&threshold=1.2128;"
Hi @icofalc , we didn't have that design in mind. However, our code does calculate false positive rate on legitimate examples, if that's what you're looking for.
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Hi @mzweilin , thanks for your reply,
Yes it could be interesting, how do we have to pass the parameters with our modified image database?
Thanks
@icofalc
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Hi @mzweilin , thanks for your reply,
Yes it could be interesting, how do we have to pass the parameters with our modified image database?
Thanks
@icofalc
I think you can implement a pseudo attack that wouldn't make any change to images, following the more complex attack examples in https://github.com/mzweilin/EvadeML-Zoo/tree/master/attacks . The False Positive Rate should be reliable.
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Hi @mzweilin , thank you!!
Issue is closed 😊
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Related Issues (17)
- image preprocessing HOT 2
- How can I use custom models rather than pre-trained models? HOT 1
- About the python version and core dumped error HOT 3
- ImportError HOT 2
- adversarial MNIST sample
- import error in main.py HOT 1
- ModuleNotFoundError: No module named 'cleverhans' HOT 2
- FileNotFoundError HOT 1
- the wrong densenet package
- what is difference between listed pretrained models HOT 1
- "releases" folder not found. HOT 2
- Where is pretrain imagenet model weight like inception_v3? HOT 2
- pre-generated adversary examples's original label HOT 1
- The score threshold selection in train phase HOT 2
- Dependency broken issue HOT 1
- How to reproduce the ImageNet results HOT 5
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