翻訳待ち：Segmentation-Guided Spatial Indexing for Generalizable and Explainable Deepfake Detection

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

[2606.00098] Segmentation-Guided Spatial Indexing for Generalizable and Explainable Deepfake Detection [Submitted on 25 May 2026] Title:Segmentation-Guided Spatial Indexing for Generalizable and Explainable Deepfake Detection View a PDF of the paper titled Segmentation-Guided Spatial Indexing for Generalizable and Explainable Deepfake Detection, by Izaldein Al-Zyoud and Abdulmotaleb El Saddik View PDF HTML (experimental) Abstract:We introduce segmentation-guided spatial indexing for generalizable and explainable deepfake detection. The key idea reverses the standard design order: rather than pooling all facial tokens and classifying afterward, we first select semantically meaningful patch tokens, then pool only those. A frozen FaRL parser assigns each DINOv3 ViT-L/16 patch token a semantic label; non-target tokens are discarded; a linear probe classifies the retained region. This spatial indexing exploits DINOv3's patch-level spatial consistency, the same property that enables emergent segmentation, to present the probe with a purer regional subspace where manipulation-relevant evidence is less diluted by whole-face cues. Region attribution is structural: when the mouth model predicts fake, the decision used only mouth tokens, not an overlaid saliency map. On Celeb-DF v2, the mouth-indexed probe achieves AUC 0.905, outperforming LipForensics (+8.1 pp) and Xception (+16.9 pp), with no DINOv3 or FaRL fine-tuning and no target-domain data. Ablations isolate the mechanism: replacing regional selection with DINOv3's CLS token drops Celeb-DF v2 AUC by 26.4 pp; replacing DINOv3 with FaRL features drops it by 20.9 pp. Both DINOv3 representation and the spatial index are independently necessary; neither alone approaches the full system. Subjects: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV) Cite as: arXiv:2606.00098 [cs.CV] (or arXiv:2606.00098v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2606.00098 arXiv-issued DOI via DataCite (pending registration) Submission history From: Izaldein Al-Zyoud [view email] [v1] Mon, 25 May 2026 17:07:00 UTC (1,534 KB) Full-text links: Access Paper: View a PDF of the paper titled Segmentation-Guided Spatial Indexing for Generalizable and Explainable Deepfake Detection, by Izaldein Al-Zyoud and Abdulmotaleb El Saddik View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-06 Change to browse by: cs eess eess.IV References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: 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 values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)