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

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 floatingpoint 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. CCS Concepts:• Computing methodologies → Neural networks; • Software and its engineering → Formal software verification.

Parametric Chordal Sparsity for SDP-based Neural Network Verification

“…While Reluplex only applies to feedforward neural networks with ReLU activation functions, the technique was extended to fully connected and convolutional neural networks with arbitrary piecewise-linear activation functions with Marabou [14]. Other works in SMT-based verification include [15], [16]. Related to SMT-solving has been the use of mixed-integer programming such as in [17], [18], [19].…”