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Deep learning model for deep fake face recognition and detection.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning, particularly generative adversarial networks (GANs), enables sophisticated image manipulation, creating deep fakes indistinguishable to humans.
  • The proliferation of deep fakes poses significant threats to public trust and security.
  • Existing deep fake detection methods often suffer from inaccuracies and high computational costs.

Purpose of the Study:

  • To develop an accurate and efficient deep learning-based method for detecting deep fake face images.
  • To address the limitations of current deep fake detection techniques, such as inaccuracy and high processing time.

Main Methods:

  • Implementation of a deep learning technique combining Fisherface with Local Binary Pattern Histogram (FF-LBPH) for face recognition and dimensionality reduction.
  • Application of Deep Belief Networks (DBN) with Restricted Boltzmann Machines (RBM) as a classifier for deep fake detection.
  • Utilizing public datasets such as FFHQ, 100K-Faces DFFD, and CASIA-WebFace for model training and evaluation.

Main Results:

  • The proposed FF-LBPH method, integrated with DBN and RBM, demonstrates effective deep fake face image detection.
  • The approach aims to overcome the inaccuracies and time-consumption issues prevalent in existing detection methods.

Conclusions:

  • The developed deep learning approach shows promise in enhancing the reliability of deep fake detection.
  • Further research in this area is crucial to combat the evolving threat of manipulated digital media.