Core Concepts
Novel approach using inductive invariants for NNCS safety verification.
Abstract
The paper introduces a compositional method for verifying safety properties of Neural Network Controlled Systems (NNCS) using inductive invariants. It addresses the challenge of verifying safety in NNCS by decomposing the inductiveness proof obligation into smaller, more manageable subproblems. The method significantly outperforms the baseline method, reducing verification time from hours to seconds.
Structure:
Abstract
Integration of neural networks into safety-critical systems
Challenge of verifying Neural Network Controlled Systems (NNCS)
Introduction of a novel approach using inductive invariants
Introduction
NNCS in safety-critical applications
Challenges in formal verification due to scale and nonlinearity of NNs
Preliminaries and Problem Statement
Symbolic Transition Systems
Invariants and Inductive Invariants
Neural Networks and NNCS
Our Approach
Compositional method for inductiveness verification
Automatic inference of generalized bridge predicates
Heuristic for falsifying inductiveness
Evaluation
Implementation details and experimental setup
Case studies on deterministic and non-deterministic 2D mazes
Comparison of monolithic and compositional methods
Related Work
Comparison with existing NN verification methods
System-level verification approaches
Automatic inductive invariant discovery
Stats
The algorithm significantly outperforms the baseline method.
Verification time reduced from hours to seconds.
Quotes
"The key idea is to decompose the monolithic inductiveness check into manageable subproblems."
"Our method allows verification of safety properties over an infinite time horizon."