AILGSEOct 9, 2022

Safety Verification for Neural Networks Based on Set-boundary Analysis

arXiv:2210.04175v17 citationsh-index: 12
Originality Incremental advance
AI Analysis

This addresses the need for rigorous safety guarantees in neural networks for applications like autonomous vehicles, but it is incremental as it builds on existing topological concepts and focuses on specific network types.

The paper tackles the safety verification problem for neural networks in safety-critical systems by proposing a set-boundary reachability method that exploits the homeomorphism property to map boundaries to boundaries, reducing computation burdens; examples demonstrate its performance, though no concrete numbers are provided.

Neural networks (NNs) are increasingly applied in safety-critical systems such as autonomous vehicles. However, they are fragile and are often ill-behaved. Consequently, their behaviors should undergo rigorous guarantees before deployment in practice. In this paper we propose a set-boundary reachability method to investigate the safety verification problem of NNs from a topological perspective. Given an NN with an input set and a safe set, the safety verification problem is to determine whether all outputs of the NN resulting from the input set fall within the safe set. In our method, the homeomorphism property of NNs is mainly exploited, which establishes a relationship mapping boundaries to boundaries. The exploitation of this property facilitates reachability computations via extracting subsets of the input set rather than the entire input set, thus controlling the wrapping effect in reachability analysis and facilitating the reduction of computation burdens for safety verification. The homeomorphism property exists in some widely used NNs such as invertible NNs. Notable representations are invertible residual networks (i-ResNets) and Neural ordinary differential equations (Neural ODEs). For these NNs, our set-boundary reachability method only needs to perform reachability analysis on the boundary of the input set. For NNs which do not feature this property with respect to the input set, we explore subsets of the input set for establishing the local homeomorphism property, and then abandon these subsets for reachability computations. Finally, some examples demonstrate the performance of the proposed method.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes