{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T02:28:26Z","timestamp":1784168906020,"version":"3.55.0"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"name":"Shashank Gupta acknowledges"},{"name":"QuNu Labs Pvt. Ltd."},{"name":"IDex Open Challenge 2.0"},{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"crossref"}]},{"name":"NSF","award":["W911NF-21-1-0264, 2241057, and 2018611"],"award-info":[{"award-number":["W911NF-21-1-0264, 2241057, and 2018611"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Digital Threats"],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>The rapid rise of deepfake technology continues to challenge digital security, trust, and misinformation control particularly for celebrities and public figures, whose identities are frequently exploited. This article introduces a novel dual paradigm deepfake detection framework that integrates a classical attention-enhanced EfficientNetB4 model with a Quantum Trained Convolutional Neural Network (QT-CNN). The classical stage leverages spatial attention and siamese feature alignment to highlight manipulation sensitive facial regions and improve cross-dataset generalization. Building on this, the QT-CNN employs parameterized quantum circuits and quantum-to-classical parameter mapping to reduce model complexity while preserving detection accuracy.<\/jats:p>\n                  <jats:p>Comprehensive experiments on a large-scale South Asian celebrity dataset, an underrepresented demographic in existing benchmarks alongside FF++ and DFDC, demonstrate that the hybrid approach achieves robust performance, including 94.5% accuracy on in-distribution data and strong generalization under demographic, corruption, and compression shifts. The QT-CNN further reduces trainable parameters by nearly 70%, suggesting a promising pathway for efficient deployment in resource-constrained, high-volume environments such as social media moderation pipelines. This work contributes a scalable, demographically inclusive, and quantum informed methodology toward securing digital ecosystems in both current and emerging post quantum environments.<\/jats:p>","DOI":"10.1145\/3794846","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T11:23:45Z","timestamp":1769772225000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing Digital Security: A Novel Dual-Paradigm Approach for Robust Deepfake Detection Using Pre and Post Quantum-Trained Neural Networks"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2004-8517","authenticated-orcid":false,"given":"Shashank","family":"Gupta","sequence":"first","affiliation":[{"name":"QuNu Labs Pvt. 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