Adversarial-Robustness-via-Enforcing-1-Lipschitz-Function
Project for NYU Foundations of Machine Learning CSCI-GA 2566 against Adversarial examples generated by AutoAttack from CIFAR-10. Our method achieve 59.50% evaluated on 1000 samples of standard mode AutoAttack, which outperforms SOTA method trained without synthetic or extra data on RobustBenchmark (58.04%).
To install
pip install -r requirement.txt
For important changes to make to run the code, please check Dependencies.txt file
To train our proposed method with Spectral Normalization and Stochastic Weights Averaging
The below command run with 2 gpus, 256 batch size, 8 workers, 200 epochs, sgd optimizer, Jenson Shannon loss, WResNet34-10 as backbone, learning rate 0.1, multi-step scheduler while using Spectral Normalization and Stochastic Weights Averaging
python cr_train.py --batch_size 256 --num_workers 8 --max_epochs 200 --gpus 2 --optimizer "sgd" --loss_func "JS" --backbone_model "WResNet" --lr 0.1 --scheduler "multistep" --use_sn --use_swa --runpath "./runs" --model_dir "JS_sn_swa"
For more argument settings, please check argument parser help and our detailed argument setup at cr_train.py and cr_pl.py
To test result with loaded model by AutoAttack
The below command run with 1 gpus, 8 workers, WResNet34-10 as backbone, load path to be "./runs/JS_swa_sn/epoch=89-step=8820.ckpt" and "./runs/MIN_swa_sn/swa.ckpt" with ensemble enabled (Note: if multiple models are loaded, their load paths are spearated by ", " while passing into --load_path argument and --ensemble need to be set), evaluted on full testset (Note: if num_examples set to -1, full testset is evaluated) and no models are not using spectral norm (Note: if some models to be loaded don't use spectral need to passing in indeces of the models that don't use spectral norm to --no_sn. For example, --no_sn "0, 1" indicate first two loaded models from --load_path are not using spectral norm, "" indicates all models are using spectral norm)
python cr_test.py --num_workers 8 --gpus 1 --backbone_model "WResNet" --load_path "./runs/JS_swa_sn/epoch=89-step=8820.ckpt, ./runs/MIN_swa_sn/swa.ckpt" --ensemble --use_sn --num_examples -1 --no_sn ""
This will create tensorboard file where you can monitor the training schedule. In the tensorboard, you can find thhe graph for adversarial attack score from AutoAttack.
To use tensorboard
tensorboard --logdir=./runs
Correspondence
Haoxu Huang (hh2740@nyu.edu), Jiraphon Yenphraphai (jy3694@nyu.edu), Haozhen Bo (hb2432@nyu.edu)