{
  "abstract": "Deepfake anomaly detection has become increasingly critical in visual security applications. However, many existing methods rely heavily on large-scale supervised forgery annotations and often struggle to capture subtle manipulation traces distributed across both spatial and frequency domains. To address these limitations, we propose a Self-Supervised Dual-Domain Alignment (SDDA) framework that jointly learns spatial and spectral representations without requiring labeled forged samples. Specifically, SDDA employs two parameter-sharing encoders to extract complementary features from RGB images and their frequency-domain counterparts, while a cross-domain alignment module enforces consistency between spatial textures and spectral signatures. To provide explicit and reproducible self-supervised supervision, four complementary descriptors are further derived from FAN feature maps: Local Structural Deviation (LSD) for local gradient inconsistency, Global Pattern Discrepancy (GPD) for holistic channel-correlation differences, Local Residual Difference (LRD) for residual-level inconsistency, and Total Consistency Deviation (TCD) for multi-layer feature deviation. These descriptors are predicted from the fused spatial–frequency embedding, encouraging the model to learn manipulation-sensitive representations in the absence of fake samples. In addition, a dual-domain contrastive objective enhances the discrimination of subtle forgery-related anomalies while improving robustness against real-world degradations such as compression and illumination variations. Experimental results demonstrate that SDDA consistently outperforms state-of-the-art baselines, particularly under challenging unseen-manipulation scenarios. Overall, the proposed framework provides an interpretable and label-efficient solution for dual-domain deepfake anomaly detection.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, Henan, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yi He"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/56b5cff193p5y.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0358049",
  "fulltext_url": null,
  "id": "56b5cff193p5y",
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    "1932-6203"
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  "landing_url": "https://doi.org/10.1371/journal.pone.0358049",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1707|Digital Media Forensic Detection"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358049&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/56b5cff193p5y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0358049",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0358049"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:bioinformatics",
      "subfield": "bioinformatics"
    },
    {
      "field": "mathematics",
      "origin": "plos:probability theory",
      "subfield": "probability"
    },
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "Dual-domain self-supervised feature alignment via spectral–spatial representation learning for deepfake anomaly detection",
  "title_text": "Dual-domain self-supervised feature alignment via spectral–spatial representation learning for deepfake anomaly detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/56b5cff193p5y",
  "venue": "PLOS One",
  "versions": []
}