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Enhancing Digital Security: A Novel Dual-Paradigm Approach for Robust Deepfake Detection Using Pre and Post Quantum-Trained Neural Networks

Shashank Gupta,Yashas Hariprasad,S. Iyengar,Subhash Gurappa,P. Mohanty

Published 2026 in Digital Threats: Research and Practice

ABSTRACT

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 paper 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. 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.

PUBLICATION RECORD

  • Publication year

    2026

  • Venue

    Digital Threats: Research and Practice

  • Publication date

    2026-01-30

  • Fields of study

    Not labeled

  • Identifiers
    DOI 10.1145/3794846
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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