@inproceedings{
suresh2024relational,
title={Relational Verification Leaps Forward with {RABB}it},
author={Tarun Suresh and Debangshu Banerjee and Gagandeep Singh},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=W5U3XB1C11}
}
We propose RABBit, a Branch-and-Bound-based verifier for verifying relational properties defined over Deep Neural Networks, such as robustness against universal adversarial perturbations (UAP). Existing SOTA complete $L_{\infty}$-robustness verifiers can not reason about dependencies between multiple executions and, as a result, are imprecise for relational verification. In contrast, existing SOTA relational verifiers only apply a single bounding step and do not utilize any branching strategies to refine the obtained bounds, thus producing imprecise results. We develop the first scalable Branch-and-Bound-based relational verifier, RABBit, which efficiently combines branching over multiple executions with cross-executional bound refinement to utilize relational constraints, gaining substantial precision over SOTA baselines on a wide range of datasets and networks. Our code is at https://github.com/uiuc-focal-lab/RABBit.
Relational Verification Leaps Forward with RABBit
Tarun Suresh Debangshu Banerjee Gagandeep Singh