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Related Experiment Videos

Attention-augmented hybrid framework with evolutionary optimization for robust deepfake detection.

S J Shivaprakash1, Sabireen H2, Akshat Chauhan1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.

Scientific Reports
|May 19, 2026
PubMed
Summary
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This study introduces a novel deepfake detection framework using a customized Gated Recurrent Unit (GRU) and evolutionary optimization. The new model achieves superior accuracy and generalization across datasets, effectively combating sophisticated deepfake threats.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Generative models like GANs and Autoencoders have increased deepfake production, threatening digital trust and enabling fraud.
  • Existing deepfake detection models lack generalizability and struggle with temporal feature modeling across diverse datasets and formats.
  • A research gap exists in developing robust and adaptable deepfake detection systems.

Purpose of the Study:

  • To design a novel deepfake detection framework with high accuracy and generalizability.
  • To enhance temporal inconsistency and facial dynamic capture in manipulated videos.
  • To create a scalable and adaptive deepfake detection solution.

Main Methods:

  • A multi-stage pipeline utilizing Vision Transformers (ViTs) for spatial features and pretrained CNNs (MobileNetV2) for feature refinement.
Keywords:
Convolutional Neural NetworksCustom Recurrent Neural NetworksDeepfake DetectionGated Recurrent UnitVision Transformers

Related Experiment Videos

  • A hybrid Gated Recurrent Unit (GRU) architecture with a deep-fake-specific gating mechanism for enhanced sequential learning.
  • Genetic Algorithm optimization for GRU parameters, layer count, and hyperparameter tuning.

    • Main Results:

    • The proposed model significantly outperforms state-of-the-art methods in accuracy, precision, recall, F1-score, and ROC-AUC.
    • Demonstrated strong generalization capabilities on unseen data, reducing false positive rates.
    • Achieved superior performance on benchmark datasets like Celeb-DF V2 and FaceForensics++.

    Conclusions:

    • The novel GRU-based framework with evolutionary optimization offers an effective and scalable approach to deepfake detection.
    • The system shows significant promise for applications in digital forensics, content verification, and policy enforcement.
    • This research addresses the need for robust deepfake detection against evolving manipulation techniques.