This repository contains the implementation for the TMLR'24 paper titled "ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations". [openreview] [arxiv]

• Python 3.8
• PyTorch 1.9.0
• wandb
• auto-attack (https://github.com/fra31/auto-attack)

To train the proposed approach, please refer to pretraining.py. To start a run, please refer to the train.sh script which by default has the reported training hyperparameters for WRN34-10 CIFAR100 setting. If the flag --linear_eval is provided, then post training, the code will automatically perform simple linear finetuning (SLF), save the linear checkpoint, and then evaluate metrics like Clean, PGD20 and GAMA accuracies (not auto-attack).

To train the baseline approach (DeACL), please refer to pretraining_deacl.py and the script train_deacl.sh on how to start the run. Rest of the workflow is the same (finetuning + evaluation).

In case one would like to perform finetuning separately post training, they can do so using the finetune.sh script which calls finetune.py with relevant hyper-parameters. Once the finetuned checkpoint is saved, as mentioned earlier, this code evalutes the model on clean, PGD20 and GAMA accuracy.

Once a finetuned checkpoint is available, one could perform AutoAttack (AA) evaluation (see aa_eval.sh and autoattack_eval.py) or GAMA evaluation (see gama_eval.py and at the end of finetune.py on how to evaluate against the GAMA attack)

This codebase borrows code from AdvCL official repository which is MIT Licensed.

This source code is released under the MIT license found in the LICENSE file.

@article{
addepalli2024profeat,
title={ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations},
author={Sravanti Addepalli and Priyam Dey and R. Venkatesh Babu},
journal={Transactions on Machine Learning Research},
year={2024},
url={https://openreview.net/forum?id=AUC0Kmn70N},
}