Interval Certifications for Multilayered Perceptrons via Lattice Traversal
In particular, we show that the adversarial robustness problem can be reduced to a lattice traversal problem. Each element of this lattice corresponds to an interval, i.e., an axis-aligned hyper-rectangle, containing an input point $\mathbf{x}$. Consider a multilayered perceptron classifier (MLP).
- ▪In particular, we show that the adversarial robustness problem can be reduced to a lattice traversal problem.
- ▪Each element of this lattice corresponds to an interval, i.e., an axis-aligned hyper-rectangle, containing an input point $\mathbf{x}$.
- ▪Consider a multilayered perceptron classifier (MLP).
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.08773 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
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Computer Science > Artificial Intelligence arXiv:2607.08773 (cs) [Submitted on 9 Apr 2026] Title:Interval Certifications for Multilayered Perceptrons via Lattice Traversal Authors:Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris, João Marques-Silva View a PDF of the paper titled Interval Certifications for Multilayered Perceptrons via Lattice Traversal, by Merkouris Papamichail and Konstantinos Varsos and Giorgos Flouris and Jo\~ao Marques-Silva View PDF HTML (experimental) Abstract:In this work we present a rigorous theoretical framework to a foundational problem of AI safety, namely adversarial robustness. In particular, we show that the adversarial robustness problem can be reduced to a lattice traversal problem.
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