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mFI-PSO — Adversarial Image Generation and Training for Deep Neural Networks

This repo is the official implementation for mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks. (Accepted by 2022 International Joint Conference on Neural Networks (IJCNN))

plot

Requirements

  • keras 2.4.0
  • tensorflow 2.3.0
  • numpy
  • xlwt
  • scipy
  • xlrd
  • seaborn
  • natsort

Instruction

Every executable file has a name that starts with a number, and this number also indicates the proper order to run the corresponding file starting from 0 to 10. Please substitute '/your_path_to_main_dir/' to your actual path to the main directory in the executable files.

Environment

Please prepare an environment with python=3.9, and then use the command "pip install -r requirements.txt" for the dependencies.

Step 1:

Directory FI_Image_Choose: 0.Cifar_set_make.py / 0.Mnist_set_make.py: download the datasets and properly preprocess them. 1.Cifar_resnet32.py / 1.Mnist_resnet32.py: train ResNet32 with original training data. 2.Cifar_pic_FI.py / 2.Mnist_pic_FI.py: Calculate pic_FI for every image in the datasets, and save them to an excel file. 3.Cifar_and_Mnist_misjudgement_heatmap.py: Determine y_target according to the misjudgement relationship. 4.Cifar_adv_sample_choose.py / 4.Mnist_adv_sample_choose.py: Choose the qualified original image for making adversarial images.

Step 2:

Directory Adv_Image_Generation: 5.Cifar_adv_set_make_PSO.py / 5.Mnist_adv_set_make_PSO.py: use PSO to make adversarial images. 6.make_adv_dataset_cifar.py / 6.make_adv_dataset_mnist.py : concatenate every adversarial image and target into one npy file.

Step 3:

Directory Sucess_Rate: 7.success_rate_cifar.py / 7.success_rate_mnist.py: calculate the success rate of the generated adversarial images.

Step 4:

Directory Defense_Training: 8.mix_original_adv_data_cifar.py / 8.mix_original_adv_data_mnist.py: mix the original training and testing data with adversarial training and testing images. 9.defense_training_cifar.py / 9.defense_training_mnist.py : retrain the original trained model with the mix of original and adversarial images.

Step 5:

Directory Calculating_Actual_Perturbation: 10.calculating_perturabtion_cifar.py / 10.calculating_perturabtion_mnist.py: calculate the actual max absolute difference summary statistics between the original image and adversarial images.

Step 6:

You may repeat Step 1 (2,3,4), Step 2, and Step 3 to re-attack the defense trained network to see the improvement in the model robustness.

Citations

@inproceedings{Shu2022,
  title={mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks},
  author={Shu, Hai and Shi, Ronghua and Jia, Qiran and Zhu, Hongtu and Chen, Ziqi},
  booktitle={2022 International Joint Conference on Neural Networks (IJCNN)},
  year={2022}
}