STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Electronics Automation Review Article

Real-Time Deepfake Detection in Video Conferencing Systems

  1. Juhi Singh Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Noida
  2. Shreya Saxena Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Noida
  3. A. Madhav Ainesh Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Noida
  4. Yanshi Goyal Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Noida

Abstract

Deepfake technology presents non-exemplary threats to video conferencing platforms, enabling advanced fraud, impression and misinformation campaigns worth billions annually. Current detection methods either exhibit latencies exceeding 100ms or rely on server-side cloud processing, raising privacy concerns. This paper presents DeepConfGuard, a lightweight hybrid architecture combining MobileNetV2 for spatial feature extraction, a bidirectional LSTM with attention for temporal modelling, and EfficientNetV2 for refinement. It reaches 94.8% accuracy with 85 ms end‑to‑end latency on about 95,000 video samples, which is suitable for real‑time deployment on edge devices. Key contributions are: (1) multi-scale CNN LSTM fusion detecting both individual frame artifacts and motion anomalies, (2) SHAP-based pixel-level explainability via heatmap overlays building operator trust, (3) Flask-SocketIO inference server enabling 30fps local processing with GDPR compliance, (4) comprehensive cross-dataset validation showing 91.2% generalization on unseen FaceForensics++and adversarial strongness improvements via knowledge distillation. Ablation studies quantify LSTM's +1.3% accuracy contribution and attention's +0.9% uplift. While maintaining detection performance and safeguarding critical meeting content, the suggested system lessens reliance on external servers. It is useful for privacy-conscious video communication environments because of its small size, which enables effective inference under constrained computer resources. The findings show a fair trade-off between deployment efficiency, interpretability, processing speed, and detection accuracy.

Keywords

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