Official code for "TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization", CVPR 2023
PyTorch >= 1.9.0
The AT_cifar and AT_high_res_data are the baseline AT for CIFAR10/100 and high-resolution image data (Caltech, CUB, Stanford-Dogs). The proposed method is in TWINS_cifar and TWINS_high_res_data. We use the pre-trained model in https://github.com/microsoft/robust-models-transfer.
If you use our code in your research, please cite with:
@inproceedings{
liu2023twins,
title={TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization},
author={Ziquan Liu and Yi Xu and Xiangyang Ji and Antoni B. Chan},
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}
We use robustness package in the robust model fine-tuning and advprop in the two-branch batch norm implementation.