Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

Guided-Denoise

The winning submission for NIPS 2017: Defense Against Adversarial Attack of team TSAIL

Paper

Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser

File Description

  • prepare_data.ipynb: generate dataset

  • Originset, Originset_test: the folder for original image

  • toolkit: the program running the attack in batch

  • Attackset: the attacks

  • Advset: the adversarial images

  • checkpoints: the models checkpoint used, download here

  • Exps: the defense model

  • GD_train, PD_train: train the defense model using guided denoise or pixel denoise

How to use

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

About

The winning submission for NIPS 2017: Defense Against Adversarial Attack of team TSAIL

Resources

Releases

No releases published

Packages

No packages published

Languages

You can’t perform that action at this time.