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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Combined spatial and frequency dual stream network for face forgery detection.

Hui Zhao1,2, Xin Li1,2, Bingxin Xu1,2

  • 1Department of Robotics, Beijing Union University, Beijing, China.

Peerj. Computer Science
|April 25, 2024
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Summary
This summary is machine-generated.

This study introduces a novel dual-stream network for face forgery detection, effectively combining spatial and frequency information. The method enhances the identification of manipulated faces, improving cybersecurity and privacy protection.

Keywords:
Cross self attentionFace forgery detectionImage frequency analysisMulti-scale feature extraction

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Area of Science:

  • Computer Vision
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Generative models enable increasingly sophisticated face manipulation and forgery.
  • Face forgery poses significant threats to politics, privacy, and cybersecurity.
  • Existing detection methods often analyze spatial and frequency domains independently, limiting performance.

Purpose of the Study:

  • To develop an effective face forgery detection method by integrating spatial and frequency domain information.
  • To improve the accuracy and robustness of detecting manipulated facial images.

Main Methods:

  • A combined spatial and frequency dual-stream network is proposed.
  • A cross self-attention (CSA) module facilitates multi-scale frequency feature fusion.
  • A frequency-guided spatial feature extraction module enhances semantic and contextual information.

Main Results:

  • The proposed dual-stream network effectively mines forgery traces through collaborative learning.
  • Comprehensive experiments demonstrate the method's effectiveness across different datasets.
  • The approach shows strong performance in both within-dataset and cross-dataset evaluations.

Conclusions:

  • The integrated spatial and frequency approach offers superior face forgery detection.
  • The developed network enhances the ability to identify manipulated facial data.
  • This method contributes to mitigating the risks associated with deepfake technology.