Unlocking Deterministic Robustness Certification on ImageNet

Kai Hu, Andy Zou, Zifan Wang, Klas Leino, Matt Fredrikson

Keywords: adversarial robustness, ImageNet, Lipschitz-based certification, ResNet, adversarial examples, ML security

TL;DR: We introduce a novel residual network architecture, LiResNet, and EMMA loss to facilitate it for scaling up deterministic robustness certification to ImageNet-scale datasets.

Abstract:

Despite the promise of Lipschitz-based methods for provably-robust deep learning with deterministic guarantees, current state-of-the-art results are limited to feed-forward Convolutional Networks (ConvNets) on low-dimensional data, such as CIFAR-10.
This paper investigates strategies for expanding certifiably robust training to larger, deeper models.
A key challenge in certifying deep networks is efficient calculation of the Lipschitz bound for residual blocks found in ResNet and ViT architectures.
We show that fast ways of bounding the Lipschitz constant for conventional ResNets are loose, and show how to address this by designing a new residual block, leading to the *Linear ResNet* (LiResNet) architecture.
We then introduce *Efficient Margin MAximization* (EMMA), a loss function that stabilizes robust training by penalizing worst-case adversarial examples from multiple classes simultaneously.
Together, these contributions yield new *state-of-the-art* robust accuracy on CIFAR-10/100 and Tiny-ImageNet under $\ell_2$ perturbations.
Moreover, for the first time, we are able to scale up fast deterministic robustness guarantees to ImageNet, demonstrating that this approach to robust learning can be applied to real-world applications.

Submission Number: 383