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.
Gupta et al. (2026) studied this question.