Some Complexity Results for Robustness Verification for Binarized Neural Networks

Harshit Goyal; Sudakshina Dutta

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arXiv · 2606.18918

Some Complexity Results for Robustness Verification for Binarized Neural Networks

Harshit Goyal·Sudakshina Dutta

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Abstract

This paper investigates the computational complexity of verification problems for Binarized Neural Networks (BNNs), in which activations and weights are binary. Specifically, we study three verification problems. First, we prove that checking the satisfiability of a linear property for a BNN is NP-complete via a reduction from the Boolean Satisfiability (SAT) problem. Second, we show that verifying robustness under non-uniform image occlusion is NP-complete through a reduction from SAT. Finally, we demonstrate that uniform occlusion induces a piecewise-constant structure in the network output, which enables the design of a polynomial-time algorithm for robustness verification.

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Harshit Goyal, Sudakshina Dutta. 2026-06-17. Some Complexity Results for Robustness Verification for Binarized Neural Networks. https://arxiv.org/abs/2606.18918

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