{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: eLight (2026)",
  "arxiv": "2605.19360",
  "author_total": 3,
  "authors": [
    {
      "name": "Parnian Ghapandar Kashani"
    },
    {
      "name": "Shiqi Chen"
    },
    {
      "name": "Aydogan Ozcan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a863a4bvq3pab.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.19360",
  "fulltext_url": null,
  "id": "a863a4bvq3pab",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.19360",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "cs.NE"
    ],
    [
      "arxiv",
      "physics.app-ph"
    ],
    [
      "arxiv",
      "physics.optics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2605.19360",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a863a4bvq3pab.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:39Z",
      "native_id": "2605.19360",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.19360"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.app-ph",
      "subfield": "applied-instrumentation"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Scalable, Energy-Efficient Optical-Neural Architecture for Multiplexed Deepfake Video Detection",
  "title_text": "Scalable, Energy-Efficient Optical-Neural Architecture for Multiplexed Deepfake Video Detection",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:39Z",
  "url": "https://research.draconitic.com/paper/a863a4bvq3pab",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1186/s43593-026-00143-y",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}