Towards Universal Certified Robustness with Multi-Norm Training
<|reference_start|>Towards Universal Certified Robustness with Multi-Norm Training: Existing certified training methods can only train models to be robust against a certain perturbation type (e.g. $l_\infty$ or $l_2$). However, an $l_\infty$ certifiably robust model may not be certifiably robust against $l_2$ perturbation (and vice versa) and also has low robustness against other perturbations (e.g. geometric transformation). To this end, we propose the first multi-norm certified training framework \textbf{CURE}, consisting of a new $l_2$ deterministic certified training defense and several multi-norm certified training methods, to attain better \emph{union robustness} when training from scratch or fine-tuning a pre-trained certified model. Further, we devise bound alignment and connect natural training with certified training for better union robustness. Compared with SOTA certified training, \textbf{CURE} improves union robustness up to $22.8\%$ on MNIST, $23.9\%$ on CIFAR-10, and $8.0\%$ on TinyImagenet. Further, it leads to better generalization on a diverse set of challenging unseen geometric perturbations, up to $6.8\%$ on CIFAR-10. Overall, our contributions pave a path towards \textit{universal certified robustness}.<|reference_end|>
@article{jiang2024towards,
    title={Towards Universal Certified Robustness with Multi-Norm Training},
    author={Enyi Jiang, Gagandeep Singh},
    journal={arXiv preprint arXiv:2410.03000},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03000},
    primaryClass={cs.LG cs.CR}
}