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A hybrid spatial-frequency attention-based algorithm using efficientnet for robust and interpretable deepfake

Mohit Kumar1, Ashwani Kumar2, Vikram Yadav3

  • 1Department of CSE, Amity University Jharkhand, Ranchi, India.

Scientific Reports
|April 8, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a hybrid deepfake detector combining spatial and frequency data for improved accuracy. The novel method enhances digital trust and forensic security against sophisticated synthetic media.

Area of Science:

  • Computer Vision
  • Digital Forensics
  • Artificial Intelligence

Background:

  • Deepfake synthesis methods pose risks to digital trust and forensic security.
  • Existing deepfake detectors struggle with robustness, generalization, and interpretability due to limited spatial or frequency domain analysis.
  • The need for advanced deepfake detection is critical in combating misinformation and ensuring media authenticity.

Purpose of the Study:

  • To introduce a generalizable hybrid spatial-frequency deepfake detector.
  • To overcome the limitations of existing methods in robustness, generalization, and interpretability.
  • To enhance the accuracy and reliability of deepfake detection for real-world forensic applications.

Main Methods:

  • A hybrid spatial-frequency deepfake detection framework combining RGB visual features and Discrete Cosine Transform (DCT) frequency elements.
Keywords:
Attention mechanismDeepfake detectionEfficientNetExplainable artificial intelligenceFaceForensics++Frequency-domain forensicsMedia forensicsSpatial–frequency fusion

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  • Utilizing an EfficientNet-B7 backbone for hierarchical feature extraction and a Convolutional Block Attention Module (CBAM) for adaptive information highlighting.
  • Early integration of spatial and frequency information to leverage semantic inconsistencies and high-frequency distortions.

    • Main Results:

    • Achieved state-of-the-art performance on the FaceForensics++C23 dataset with a ROC-AUC of 0.997.
    • Demonstrated high precision-recall balance and efficient training convergence.
    • Feature-space analysis and CAM-based visualizations confirmed high class separability and identified manipulation-prone regions.

    Conclusions:

    • The proposed hybrid framework offers high detection accuracy, improved generalization potential, and enhanced interpretability.
    • The method effectively addresses the limitations of traditional spatial or frequency-domain detectors.
    • The framework is suitable for real-life deepfake forensics, bolstering digital trust and media authenticity.