Code base for the paper Perturbation Type Categorization for Multiple Adversarial Perturbation Robustness
CUDA_VISIBLE_DEVICES=0 python pc_train.py --config configs/CIFAR10_pipeline.json
Additional Parameters:
model_id: Unique id for the model
opt_type: SGD or Adam
fft: 0 or 1 (To use fourier transform or not)
epochs: Number of epochs to train
num_iter: Number of iterations for the attack
model_type: Type of model to train
batch_size: Batch size
lr_max: Maximum learning rate
lr_mode: 1 for linear, 2 for cosine
droprate: Dropout rate
attacked_model_list: List of models to attack
attack_types: List of attack types
python test.py --config configs/MNIST_small_step.json --num_iter 200 --model_type cnn_msd --path models/m_cnn/Baselines/max --mode base --restarts 2 --attack pgd --batch_size 500 --attack_types linf l1 l2 ddn
@inproceedings{
maini2022perturbation,
title={Perturbation Type Categorization for Multiple Adversarial Perturbation Robustness},
author={Pratyush Maini and Xinyun Chen and Bo Li and Dawn Song},
booktitle={The 38th Conference on Uncertainty in Artificial Intelligence},
year={2022},
url={https://openreview.net/forum?id=BlbhyDUo9xc}
}