Safety Verification for Neural Networks Based on Set-boundary Analysis
Zhen Liang, Dejin Ren, Wanwei Liu, Ji Wang, Wenjing Yang, Bai Xue

TL;DR
This paper introduces a set-boundary analysis method for safety verification of neural networks, leveraging topological properties to efficiently determine if network outputs stay within safe bounds, especially for invertible NNs.
Contribution
The paper proposes a novel set-boundary reachability approach utilizing the homeomorphism property of certain neural networks to improve safety verification efficiency.
Findings
Effective in verifying safety of invertible neural networks.
Reduces computational burden by focusing on input set boundaries.
Demonstrated success on example networks.
Abstract
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…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · S100 Proteins and Annexins · Fault Detection and Control Systems
