davidsonic/Interpretable_CNN

This repository is deprecated, please go to https://github.com/davidsonic/Interpretable_CNNs_via_Feedforward_Design

Interpretable_CNN

This part contains the code for adversarial attack in the paper Interpretable Convolutional Neural Networks via Feed Forward Design, maintained by Jiali Duan and Min Zhang.

Table of Content

• [Requirements]

  • Python3, keras, tensorflow, cleverhans (Refer to https://github.com/tensorflow/cleverhans), pickle

• [Function]

  • BP/ff models are provided for cifar10 and mnist dataset under folder dataset_structure_model
  • Models can be trained from scratch if no filename is specified
  • By changing adversarial attack methods, different algorithms can be tested
  • Refer to show_sample.ipynb to visualize generated adversarial samples

• [Usage]

  • python cifar_keras.py -train_dir cifar_BP_model -filename cifar.ckpt -method FGSM
  • python cifar_keras.py -train_dir cifar_ff_model -filename FF_init_model.ckpt -method BIM

cifar_keras.py:
  --batch_size: Size of training batches
    (default: '128')
    (an integer)
  --filename: Checkpoint filename.
    (default: 'FF_init_model.ckpt')
  --learning_rate: Learning rate for training
    (default: '0.001')
    (a number)
  --[no]load_model: Load saved model or train.
    (default: 'true')
  --method: Adversarial attack method
    (default: 'FGSM')
  --nb_epochs: Number of epochs to train model
    (default: '40')
    (an integer)
  --train_dir: Directory where to save model.
    (default: 'cifar_ff_model')