The winning submission for NIPS 2017: Defense Against Adversarial Attack of team TSAIL
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
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prepare_data.ipynb: generate dataset
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Originset, Originset_test: the folder for original image
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toolkit: the program running the attack in batch
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Attackset: the attacks
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Advset: the adversarial images
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checkpoints: the models checkpoint used, download here
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Exps: the defense model
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GD_train, PD_train: train the defense model using guided denoise or pixel denoise
the attacks are stored in folder Attackset the script is in the toolkit folder. in the run_attacks.sh file: modify models to the attacks you want to generate, separate by comma, or use "all" to include all attacks in Attackset. use the command to run:
bash run_attacks.sh $gpuids
where gpuids is the id of the gpus you want to use, they are number separated by comma. It will generate the training set.
Then change the line DATASET_DIR="${parentdir}/Originset" to DATASET_DIR="${parentdir}/Originset_test", and run the command bash run_attacks.sh $gpuids again.
Then specify a model you want to use, the models are stored in Exp folder, there is a sample folder, it refers to a model named "sample", let's use it. Then go to GD_train if you want to use guided denoiser, run
python main --exp sample
The program will load Exp/sample/model.py as a model to train. and also you can specify other parameters defined in the GD_train/main.py