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Cost-Sensitive Robustness against Adversarial Examples

The goal of this project (link to the original ArXiv paper):

  • For any pre-designed cost matrix, define the cost-sensitive robustness for neural network classifiers
  • Develop a general method for training certified cost-sensitive robust classifier against l_infty adversarial attack
  • Compare with existing certified overall robust classifier on MNIST and CIFAR-10 datasets

Installation & Usage

The code was developed using Python3 on Anaconda

  • Install Pytorch 0.4.1:
conda update -n base conda && conda install pytorch=0.4.1 torchvision -c pytorch -y
  • Install convex_adversarial package developed by Eric Wong and Zico Kolter [see details]:
pip install --upgrade pip && pip install convex_adversarial==0.3.5 -I --user torch==0.4.1
  • Install other dependencies:
pip install torch waitGPU setproctitle
  • Examples for training the cost-sensitive robust classifier:
    cd examples && python mnist_task_spec.py --type real --category small-large --tuning coarse
    
    cd examples && python cifar_task_spec.py --model large --type binary --category single_pair
    

What is in this respository?

  • examples, including:

    • problems.py: defines the dataloaders and neural network architectures for MNIST and CIFAR-10
    • trainer.py: implements the detailed training and evaluation functions for different classifiers
    • mnist.py, cifar.py: main functions for training overall robust classifier
    • mnist_task_spec.py, cifar_task_spec.py: main functions for training cost-sensitive robust classifier
    • heatmap.py: implements functions for generating heatmap for any given cost matrix
  • main_plot_overall.py: produces the robust heatmap for overall robust classifier

  • main_stats_mnist.py: computes the summary statistics for both robust classifiers on MNIST

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A general method for training cost-sensitive robust classifier

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