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FL-TENB4: A Federated-Learning-Enhanced Tiny EfficientNetB4-Lite Approach for Deepfake Detection in CCTV

Jimin Ha1, Abir El Azzaoui1, Jong Hyuk Park1

  • 1Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
Summary

This study introduces FL-TENB4, a new system for detecting deepfakes in CCTV footage using Tiny Machine Learning (TinyML) and Federated Learning (FL). It offers real-time, privacy-preserving deepfake detection for resource-limited cameras.

Keywords:
CCTV environmentEfficientNetB4Federated LearningTinyMLdeepfake detection

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Area of Science:

  • Computer Vision and Artificial Intelligence
  • Cybersecurity and Privacy

Background:

  • CCTV systems are crucial for public safety but vulnerable to deepfake manipulation.
  • Existing deepfake detection methods are computationally intensive, hindering real-time CCTV application.
  • Deepfakes threaten the integrity of video evidence and personal privacy.

Purpose of the Study:

  • To develop an efficient and privacy-preserving deepfake detection framework for CCTV.
  • To address the limitations of current deepfake detection solutions in resource-constrained environments.

Main Methods:

  • Proposed FL-TENB4 framework integrating Tiny Machine Learning (TinyML) with EfficientNetB4-Lite.
  • Utilized Federated Learning (FL) for privacy-preserving, collaborative model training.
  • Implemented a lightweight model optimized for edge devices and real-time processing.

Main Results:

  • FL-TENB4 demonstrated high deepfake detection accuracy on the FaceForensics++ dataset.
  • Achieved significantly reduced model size and low inference latency.
  • Validated suitability for real-world, resource-constrained CCTV environments.

Conclusions:

  • FL-TENB4 offers an effective solution for real-time deepfake detection in CCTV systems.
  • The framework balances performance, efficiency, and data privacy.
  • Enables enhanced security and reliability of surveillance systems against deepfake threats.