Towards an Effective and Interpretable Refinement Approach for DNN Verification

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.