vivianwu0512 / deepcert Goto Github PK

1. The code is tested with python3 and TensorFlow v1.10 and v1.12 (TensorFlow v1.8 is not compatible). The following packages are required.

conda create --name deepcert python=3.6
source activate deepcert
conda install pillow numpy scipy pandas h5py tensorflow numba posix_ipc matplotlib

2. Ready to run our project.

python pymain.py

It will provide the results of mnist_cnn_8layer_5_3 and lenet with sigmoid, tanh and arctan as activation function.

Results of robustness certificate and runtime are saved into log_pymain_{timestamp}.txt

We have provided a interfacing file pymain.py to compute certified bounds. This file contains a function run_cnn that computes DeepCert bounds and runtime averaged over a given number of samples.

Usage: run_cnn(file_name, n_samples, norm, core, activation, cifar)

• file_name: file name of network to certify
• n_samples: number of samples to certify
• norm: p-norm, 1,2 or i (infinity norm)
• core: True or False, use True for networks with only convolution and pooling
• activation: followed by a activation function of network- use ada for adaptive relu bounds (relu, ada, tanh, sigmoid or arctan)
• cifar: True or False, uses MNIST if False

We provide pre-trained models here. The pre-trained models use the follwing naming convention:

Pure CNN Models:

{dataset}_cnn_{number of layers}layer_{filter size}_{kernel size}[_{non ReLU activation}]
Example: mnist_cnn_4layer_10_3
Example: cifar_cnn_5layer_5_3_tanh

General CNN Models:

{dataset}_cnn_{number of layers}layer[_{non ReLU activation}]
Example: mnist_cnn_7layer
Example: mnist_cnn_7layer_tanh

Fully connected Models:

{dataset}_{number of layers}layer_fc_{layer size}
Example: mnist_2layer_fc_20

Resnet Models:

{dataset}_resnet_{number of residual blocks}[_{non ReLU activation}]
Example: mnist_resnet_3
Example: mnist_resnet_4_tanh

LeNet Models:

{dataset}_cnn_lenet[_nopool][_{non ReLU activation}]
Example: mnist_cnn_lenet_tanh
Example: mnist_cnn_lenet_nopool

If you want to run CNN-Cert, you can refer to https://github.com/AkhilanB/CNN-Cert.git. Our environment "deepcert" can also support CNN-Cert.

If you want to run FROWN, you can use cnn_to_mlp.py to transfer pure cnns to mlps, then clone the tool FROWN:

FROWN's code is tested with python 3.7.3, Pytorch 1.1.0 and CUDA 9.0. Run the following to install pytorch and its CUDA toolkit:

conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

Then clone this repository:

git clone https://github.com/ZhaoyangLyu/FROWN.git
cd FROWN

Then refer to the README.md in folder "FROWN".