{"slug": "scalable-efficient-optical-architecture-for-muxed-deepfake-video-detection", "title": "Scalable, Efficient Optical Architecture for Muxed Deepfake Video Detection", "summary": "A hybrid digital-analog deepfake video detection framework combining a lightweight digital front-end with a spatially multiplexed optical decoding back-end achieved 97.79% average detection accuracy, 99.86% sensitivity and 95.72% specificity on the Celeb-DF video dataset, processing 15 videos in parallel in a single optical pass per inference, according to a paper submitted to arXiv on 19 May 2026. The system uses a programmable spatial light modulator to run massively parallel analog inference on 15 or more video streams simultaneously, and the authors report the multiplexed optical decoder resists video degradation, noise, compression, experimental misalignments and black-box adversarial attacks. The authors state that integrating optical computation into AI inference enables simultaneous gains in throughput, energy efficiency and adversarial robustness that are difficult to achieve together in purely digital systems.", "body_md": "# Computer Science > Computer Vision and Pattern Recognition\n\n  [Submitted on 19 May 2026]\n\n# Title:Scalable, Energy-Efficient Optical-Neural Architecture for Multiplexed Deepfake Video Detection\n\n[View PDF](https://arxiv.org/pdf/2605.19360)\n\nAbstract:The rapid proliferation of AI-generated visual media has created an urgent need for efficient, trustworthy deepfake detection systems. However, existing deep learning-based detection methods rely on computationally intensive and energy-demanding inference algorithms, limiting their scalability. Here, we present a hybrid digital-analog deepfake video detection framework that combines a lightweight digital front-end with a spatially multiplexed optical decoding back-end for massively parallel analog inference through a programmable spatial light modulator. By simultaneously processing 15 or more video streams within a single optical propagation pass, the system enables high-throughput and accurate video-level authenticity prediction at reduced computational cost compared with purely digital methods. We validated this hybrid deepfake video processor using different datasets spanning classical face-swapping, real-world deepfake recordings, and fully AI-generated videos. Using a spatially multiplexed experimental set-up operating in the visible spectrum, we achieved average deepfake detection accuracy, sensitivity and specificity of 97.79%, 99.86% and 95.72%, respectively, on the Celeb-DF video dataset with 15 videos tested in parallel in a single optical pass per inference. The multiplexed optical decoder also demonstrates resilience against various types of video degradation, noise, compression, experimental misalignments and black-box adversarial attacks. Our results show that integrating optical computation into AI inference enables simultaneous gains in throughput, energy efficiency, and adversarial robustness - three properties that are difficult to achieve together in purely digital systems.\n    \n\n### Current browse context:\n\ncs.CV\n\n    Change to browse by:\n    \n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth 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.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/scalable-efficient-optical-architecture-for-muxed-deepfake-video-detection", "canonical_source": "https://arxiv.org/abs/2605.19360", "published_at": "2026-10-05 09:03:15+00:00", "updated_at": "2026-10-05 09:22:41.204596+00:00", "lang": "en", "topics": ["computer-vision", "artificial-intelligence", "ai-research", "ai-safety"], "entities": ["arXiv", "Celeb-DF", "spatial light modulator"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/scalable-efficient-optical-architecture-for-muxed-deepfake-video-detection", "markdown": "https://wpnews.pro/news/scalable-efficient-optical-architecture-for-muxed-deepfake-video-detection.md", "text": "https://wpnews.pro/news/scalable-efficient-optical-architecture-for-muxed-deepfake-video-detection.txt", "jsonld": "https://wpnews.pro/news/scalable-efficient-optical-architecture-for-muxed-deepfake-video-detection.jsonld"}}