Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2023

Abstract

Recently, several abstraction refinement techniques have been proposed to improve the verification precision for deep neural networks (DNNs). However, these techniques usually take many refinement steps to verify a property and the refinement decision in each step is hard to interpret, thus hindering their analysis, reasoning and optimization.In this work, we propose SURGEON, a novel DNN verification refinement approach that is both effective and interpretable, allowing analyst to understand why and how each refinement decision is made. The main insight is to leverage the ‘interpretable’ nature of debugging processes and formulate the verification refinement problem as a debugging problem. Given a failed verification procedure, SURGEON refines it in an iterative manner and, in each iteration, it effectively identifies the root cause of the failure and heuristically generates fixes according to abstract transformers.We have implemented SURGEON in a prototype and evaluated it using a set of local robustness verification problems. Besides the interpretability, the experimental results show our approach can improve the precision of base verification methods and is more effective than existing refinement techniques.

Keywords

abstraction debugging, abstraction refinement, neural network verification

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS): Chang Mai, October 22-26: Proceedings

First Page

569

Last Page

580

ISBN

9798350319583

Identifier

10.1109/QRS60937.2023.00062

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/QRS60937.2023.00062

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