Certified geometric robustness -- Super-DeepG

Computer Science > Artificial Intelligence arXiv:2604.24379 (cs) [Submitted on 27 Apr 2026] Title:Certified geometric robustness -- Super-DeepG Authors:Noémie Cohen, Mélanie Ducoffe (Airbus CR\&T), Christophe Gabreau, Claire Pagetti, Xavier Pucel View a PDF of the paper titled Certified geometric robustness -- Super-DeepG, by No\'emie Cohen and 4 other authors View PDF Abstract:Safety-critical applications are required to perform as expected in normal operations. Image processing functions are often required to be insensitive to small geometric perturbations such as rotation, scaling, shearing or translation. This paper addresses the formal verification of neural networks against geometric perturbations on their image dataset. Our method Super-DeepG improves the reasoning used in linear relaxation techniques and Lipschitz optimization, and provides an implementation that leverages GPU hardware. By doing so, Super-DeepG achieves both precision and computational efficiency of robustness certification, to an extent that outperforms prior work. Super-DeepG is shared as an open-source tool on GitHub. Comments: ICCPS / HSCC 2026, CPS IoT Week, May 2026, Saint Malo (Palais du Grand Large), France Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Symbolic Computation (cs.SC) Cite as: arXiv:2604.24379 [cs.AI] (or arXiv:2604.24379v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.24379 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Claire Pagetti [view email] [via CCSD proxy] [v1] Mon, 27 Apr 2026 12:10:06 UTC (1,126 KB) Full-text links: Access Paper: View a PDF of the paper titled Certified geometric robustness -- Super-DeepG, by No\'emie Cohen and 4 other authorsView PDFTeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs cs.LG cs.SC References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these…

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