Enhancing Digital Security: A Novel Dual-Paradigm Approach for Robust Deepfake Detection Using Pre and Post Quantum-Trained Neural Networks
Article
Gupta, S, Hariprasad, Y, Iyengar, SS et al. (2026). Enhancing Digital Security: A Novel Dual-Paradigm Approach for Robust Deepfake Detection Using Pre and Post Quantum-Trained Neural Networks
. Digital Threats: Research and Practice, 7(1), 10.1145/3794846
Gupta, S, Hariprasad, Y, Iyengar, SS et al. (2026). Enhancing Digital Security: A Novel Dual-Paradigm Approach for Robust Deepfake Detection Using Pre and Post Quantum-Trained Neural Networks
. Digital Threats: Research and Practice, 7(1), 10.1145/3794846
Gupta, S; Hariprasad, Y; Iyengar, SS; Gurappa, S; Mohanty, P
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 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. 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.
