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QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking
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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
Forward citations
Cited by 1 Pith paper
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Lipschitz-Based Robustness Certification Under Floating-Point Execution
Lipschitz-based robustness certificates that assume real arithmetic can be unsound under floating-point execution; a formal FP-aware theory and certifier close that gap for dense ReLU networks.
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