Parametric Chordal Sparsity for SDP-based Neural Network Verification

Abstract

Many future technologies rely on neural networks, but verifying the correctness of their behavior remains a major challenge. It is known that neural networks can be fragile in the presence of even small input perturbations, yielding unpredictable outputs. The verification of neural networks is therefore vital to their adoption, and a number of approaches have been proposed in recent years. In this paper we focus on semidefinite programming (SDP) based techniques for neural network verification, which are particularly attractive because they can encode expressive behaviors while ensuring a polynomialtime decision. Our starting point is the DeepSDP framework proposed by Fazlyab et al, which uses quadratic constraints to abstract the verification problem into a large-scale SDP. When the size of the neural network grows, however, solving this SDP quickly becomes intractable. Our key observation is that by leveraging chordal sparsity and specific parametrizations of DeepSDP, we can decompose the primary computational bottleneck of DeepSDP -a large linear matrix inequality (LMI) -into an equivalent collection of smaller LMIs. Our parametrization admits a tunable parameter, allowing us to trade-off efficiency and accuracy in the verification procedure. We call our formulation Chordal-DeepSDP, and provide experimental evaluation to show that it can: (1) effectively increase accuracy with the tunable parameter and (2) outperform DeepSDP on deeper networks.

Risk verification of stochastic systems with neural network controllers

“…Most work falls into the open-loop verification category, which seeks to analyze the ML component on its own. For analyzing deep neural networks, most works focus on checking some form of input-output reachability [10,[26][27][28][29][30]. In addition, work has gone into estimating the Lipschitz constants of deep neural networks [31].…”