Awesome
Feature Distillation
This is an implementation of our work "Feature Distillation DNN-Oriented JPEG Compression Against Adversarial Examples" https://arxiv.org/pdf/1803.05787.pdf
- We add our feature distillation defense method in ./utils
- Thanks for the decent code from EvadeML-Zoo. This work is extended from EvadeML-Zoo (https://github.com/mzweilin/EvadeML-Zoo)
- If you use this code please cite our paper.
Cite this work
@article{liu2018feature,
title={Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples},
author={Liu, Zihao and Liu, Qi and Liu, Tao and Wang, Yanzhi and Wen, Wujie},
journal={arXiv preprint arXiv:1803.05787},
year={2018}
}
1. Install dependencies.
pip install -r requirements_cpu.txt
If you are going to run the code on GPU, install this list instead:
pip install -r requirements_gpu.txt
2. Fetch submodules.
git submodule update --init --recursive
3. Download pre-trained models.
mkdir downloads; curl -sL https://github.com/mzweilin/EvadeML-Zoo/releases/download/v0.1/downloads.tar.gz | tar xzv -C downloads
4. Usage of python main.py
usage: python main.py [-h] [--model_name MODEL_NAME]
[--select [SELECT]] [--noselect] [--nb_examples NB_EXAMPLES]
[--balance_sampling [BALANCE_SAMPLING]] [--nobalance_sampling]
[--test_mode [TEST_MODE]] [--notest_mode] [--attacks ATTACKS]
optional arguments:
-h, --help show this help message and exit
--model_name MODEL_NAME
Supported: cleverhans, cleverhans_adv_trained and
carlini for MNIST; carlini and DenseNet for CIFAR-10;
ResNet50, VGG19, Inceptionv3 and MobileNet for
ImageNet.
--select [SELECT] Select correctly classified examples for the
experiement.
--noselect
--nb_examples NB_EXAMPLES
The number of examples selected for attacks.
--balance_sampling [BALANCE_SAMPLING]
Select the same number of examples for each class.
--nobalance_sampling
--test_mode [TEST_MODE]
Only select one sample for each class.
--notest_mode
--attacks ATTACKS Attack name and parameters in URL style, separated by
semicolon.
5. Example.
python main.py --model_name MobileNet --nb_examples 100 --attacks "fgsm?eps=0.0078"