{
  "abstract": "This study evaluates an Xception-based Multitask Self-Supervised Learning (MSSL) framework for detecting real and synthetic visual content across image and video modalities. Separate models were developed for CIFAKE images and FaceForensics++ videos. Beyond the primary binary-classification objective, MSSL integrates three auxiliary representation-learning tasks: rotation prediction, patch reconstruction, and artifact-map prediction. These tasks are intended to encourage the model to learn robust semantic and manipulation-related visual features. Experiments used class-balanced subsets of 5,000 CIFAKE images and 840 FaceForensics++ videos. For video classification, five frames were selected through uniform temporal sampling, and frame-level probabilities were averaged to obtain video-level predictions; a three-frame setting was also examined. A fixed decision threshold of 0.50 was defined a priori and applied consistently across both modalities. On CIFAKE, Xception+MSSL achieved 92.60% accuracy and 0.9797 ROC-AUC, outperforming the baseline Xception model, which obtained 79.60% accuracy and 0.8937 ROC-AUC. On FaceForensics++, five-frame MSSL achieved 68.25% video-level accuracy and 0.7365 ROC-AUC, compared with 62.70% and 0.6652 for Xception. The results indicate consistent within-dataset improvements, although cross-dataset and cross-generator generalization remain unverified.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Iksan Nur Rochim"
    },
    {
      "name": "R. Rizal Isnanto"
    },
    {
      "name": "Aris Sugiharto"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8e5cfqs7ex950.bib",
  "cited_by": 0,
  "doi": "10.63158/journalisi.v8i5.1864",
  "fulltext_url": "https://journal-isi.org/index.php/isi/article/view/1864",
  "id": "8e5cfqs7ex950",
  "issns": [
    "2656-5935",
    "2656-4882"
  ],
  "landing_url": "https://doi.org/10.63158/journalisi.v8i5.1864",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QA1-939"
    ],
    [
      "lcc",
      "QA75.5-76.95"
    ],
    [
      "openalex",
      "1707|Digital Media Forensic Detection"
    ]
  ],
  "openalex": "W7223412713",
  "pdf_url": "https://journal-isi.org/index.php/isi/article/download/1864/1080",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "Asosiasi Doktor Sistem Informasi Indonesia",
  "ris": "https://research.draconitic.com/paper/8e5cfqs7ex950.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:46Z",
      "native_id": "84b81540904941c1bf671a774ac42ae4",
      "source": "doaj",
      "url": "https://doi.org/10.63158/journalisi.v8i5.1864"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "lcc:QA1-939",
      "subfield": "general"
    }
  ],
  "title": "Evaluating Xception with Multitask Self-Supervised Learning for AI-Generated Image and Deepfake Video Detection",
  "title_text": "Evaluating Xception with Multitask Self-Supervised Learning for AI-Generated Image and Deepfake Video Detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:46Z",
  "url": "https://research.draconitic.com/paper/8e5cfqs7ex950",
  "venue": "Journal of Information Systems and Informatics",
  "versions": []
}