We propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze the network. If the robustness property cannot be verified, the result is inconclusive. Due to the over-approximation, the computed region in the abstraction  may be spurious in the sense that it does not contain any true counterexample. Our goal is to identify such spurious regions and use them to guide the abstraction refinement. The core idea is to make use of the obtained  constraints of the abstraction to infer new bounds for the neurons. This is achieved by linear programming techniques.  With the new bounds, we iteratively apply DeepPoly, aiming to  eliminate spurious regions. We have implemented our approach in a prototypical tool DeepSRGR. Experimental results show that a large amount of regions can be identified as spurious, 
and as a result, the precision of DeepPoly can be significantly improved. As a side contribution, we show that our approach can be applied to verify quantitative robustness properties.

Improving Neural Network Verification through Spurious Region Guided Refinement

ETAPS is a confederation of several conferences, each with its own Programme Committee and Steering Committee. ETAPS is the most important and visible annual European event related to software sciences. Altogether, more than 500 researchers participate in this event every year. The confederated conferences cover various aspects of software systems, ranging from theoretical foundations to programming language developments, compiler advancements, analysis tools, formal approaches to software engineering, and security.

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