Initial commit

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Huaxia Wang, Chun-Nam Yu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,101 @@
+# Robust Deep Learning with Adversarial Networks
+
+This is the code accompanying the paper [A Direct Approach to Robust Deep Learning Using Adversarial Networks](https://openreview.net/forum?id=S1lIMn05F7&noteId=H1lmqBBmxV). 
+
+```
+@INPROCEEDINGS{Wang2019ICLR,
+  title={A Direct Approach to Robust Deep Learning Using Adversarial Networks},
+  author={Huaxia Wang and Chun-Nam Yu},
+  booktitle={International Conference on Learning Representations},
+  year={2019}
+}
+```
+
+
+## Getting Started
+
+The code is divided into 4 subdirectories based on experiments on 4 different datasets: MNIST, SVHN, CIFAR-10 and CIFAR-100. 
+The subdirectories share substantial amount of code and differ mostly in data processing, neural network model definitions, and training parameter settings. 
+The experiments were run with Tensorflow 1.10. 
+We include code for training standard models, adversarial training with projected gradient descent, our adversarial networks approach, and our implementation of ensemble adversarial training. 
+
+
+### Data Preparation
+
+For the [SVHN](http://ufldl.stanford.edu/housenumbers/) dataset download the cropped digits (Format 2) and put them in a folder called **SVHN**. 
+
+
+For the [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) downlaod the binary version of the data and extract in the **data** folder. 
+
+For the [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) downlaod the binary version of the data and extract in the **cifar100_expr** folder. 
+## Running the code
+
+We give examples on how to run the code through the CIFAR-10 dataset. The commands for running the experiments on other datasets are similar. 
+
+### Training the models
+
+
+To run training on standard (undefended) neural network models: 
+```
+python run_standard_cifar10.py --learning-rate=0.1 --weight-decay=1E-4 --batch-size=64 --model-file=model_std_cifar10.ckpt 
+```
+The details of these parameters and command line options can be found in the script **run_standard_cifar.py** and are largely self-explanatory. They can also be directly set in the scripts. Similarly to run adversarial training with PGD, use the script **run_pgd_cifar.py**. 
+
+```
+python run_pgd_cifar10.py --learning-rate=0.1 --weight-decay=1E-4 --epsilon=0.0625 --batch-size=64 --model-file=model_std_cifar10.ckpt 
+```
+
+The neural network architecture definitions, for both the generative and discriminative networks, are stored in the folder 'adversarial_networks/models/'. 
+New architectures can be defined and stored in that folder. 
+
+
+To run training with adversarial networks: 
+```
+python run_gan_cifar10.py --learning-rate=0.1 --weight-decay=1E-4 --epsilon=0.0625 --batch-size=64 --model-file=model_std_cifar10.ckpt 
+```
+
+
+### Evaluating the models using black box and white box attacks
+
+```
+python black_box_cifar10.py --model-file1=model1.ckpt --model-file2=model2.ckpt --epsilon=0.0625
+```
+
+The script evaluates **model1.ckpt** using adversarial examples generated from **model2.ckpt** with FGS and PGD (black box attacks). 
+The model files **model1.ckpt** and **model2.ckpt** are generated from the training commands above. 
+To perform white box attacks just use the same model file for **model-file1** and **model-file2**. 
+
+
+### Training the ensemble adversarial training (EAT) models
+
+Training ensemble adversarial training (EAT) models takes two steps. 
+First we need to generate a dataset augmented with adversarial examples: 
+
+```
+python ensemble_examples_wrapper.py --model-file=model.ckpt --sample-file=adv_cifar10.npz --test-sample-file=adv_cifar10_test.npz --epsilon=0.0625
+```
+
+This script generates adversarial examples using FGS, PGD, and least likely class with a previously trained model **model.ckpt**, and saves the generated examples along with original data in **adv_cifar10.npz** for training and **adv_cifar10_test.npz** for testing. 
+
+To train the actual EAT model, run
+```
+python run_eat_cifar10.py --eat-train-data=adv_cifar10.npz --eat-test-data=adv_cifar10_test.npz --model-file=model_eat_cifar10.ckpt
+```
+
+
+
+## Authors
+
+* Huaxia Wang
+
+* Chun-Nam Yu
+
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
+
+## Acknowledgments
+
+* The original version of this code was based on [The Numerics of GANs](https://github.com/LMescheder/TheNumericsOfGANs) . 
+