Some Complexity Results for Robustness Verification for Binarized Neural Networks
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Computer Science > Machine Learning
Title:Some Complexity Results for Robustness Verification for Binarized Neural Networks
Abstract:This paper studies the computational complexity of verification problems for Binarized Neural Networks (BNNs), where activations (and sometimes weights) are binary. We analyze two problems: satisfiability and robustness under uniform image occlusion. We show that BNN satisfiability is NP-complete via a reduction from Boolean satisfiability problem (SAT), and that uniform occlusion induces a piecewise-constant structure in the network output, enabling a polynomial-time robustness-checking algorithm.
| Subjects: | Machine Learning (cs.LG); Computational Complexity (cs.CC) |
| Cite as: | arXiv:2606.18918 [cs.LG] |
| (or arXiv:2606.18918v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18918
arXiv-issued DOI via DataCite (pending registration)
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