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+# Adversarial Training
+
+Code base for the paper [Perturbation Type Categorization for Multiple Adversarial Perturbation
+Robustness](https://proceedings.mlr.press/v180/maini22a/maini22a.pdf)
+
+## Training a Model
+
+```
+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
+
+
+## Evaluating a Model
+```
+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
+```
+
+### How can I Cite this work?
+
+```
+@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}
+}
+```