Proof Transfer for Neural Network Verification
Year: 2021
Type: preprint
Abstract: We introduce the novel concept of proof transfer for neural network verification. We show that by generating proof templates that capture and generalize existing proofs, we can speed up subsequent proofs. In particular we create these templates from previous proofs on the same neural network and consider two cases: (i) where the proofs are created online when verifying other properties and (ii) where the templates are created offline using a dataset. We base our methods on three key hypotheses of neural network robustness proofs. Our evaluation shows the potential of proof transfer for benefitting robustness verification of neural networks against adversarial patches, geometric, and $\ell_{\infty}$-perturbations.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3201426548