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arXiv · 2111.13110

QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking

Xidan Song·Edoardo Manino·Luiz Sena·Erickson Alves·Eddie de Lima Filho·Iury Bessa·Mikel Lujan·Lucas Cordeiro
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Abstract

QNNVerifier is the first open-source tool for verifying implementations of neural networks that takes into account the finite word-length (i.e. quantization) of their operands. The novel support for quantization is achieved by employing state-of-the-art software model checking (SMC) techniques. It translates the implementation of neural networks to a decidable fragment of first-order logic based on satisfiability modulo theories (SMT). The effects of fixed- and floating-point operations are represented through direct implementations given a hardware-determined precision. Furthermore, QNNVerifier allows to specify bespoke safety properties and verify the resulting model with different verification strategies (incremental and k-induction) and SMT solvers. Finally, QNNVerifier is the first tool that combines invariant inference via interval analysis and discretization of non-linear activation functions to speed up the verification of neural networks by orders of magnitude. A video presentation of QNNVerifier is available at https://youtu.be/7jMgOL41zTY

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Xidan Song, Edoardo Manino, Luiz Sena, Erickson Alves, Eddie de Lima Filho, Iury Bessa, Mikel Lujan, Lucas Cordeiro. 2021-11-25. QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking. https://arxiv.org/abs/2111.13110

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