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Deep fake detection using a sparse auto encoder with a graph capsule dual graph CNN.

Venkatachalam Kandasamy1, Štěpán Hubálovský1, Pavel Trojovský2

  • 1Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, Czech Republic.

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Summary
This summary is machine-generated.

This study introduces a novel deepfake detection method using a sparse autoencoder with graph long short-term memory (SAE-GLSTM) to identify sophisticated AI-generated image and video manipulations. The new approach effectively detects various deepfake types, enhancing digital media security.

Keywords:
Capsule convolution neural networkDeep learningDeepFakeGenerative adversarial networksGraph LSTMLong short term memory (LSTM)

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Deepfakes (DF) are AI-generated manipulated media used for misinformation and privacy breaches.
  • Existing deepfake detection methods struggle with novel manipulation techniques and advanced generative models like GANs.
  • There is a critical need for robust detection systems against evolving deepfake threats.

Purpose of the Study:

  • To develop a novel and effective method for detecting various types of deepfakes (DF) in images and videos.
  • To address the limitations of current detection methods that become obsolete against new deepfake generation techniques.
  • To enhance the security and trustworthiness of digital media against sophisticated AI-driven manipulations.

Main Methods:

  • A novel deepfake detection model is proposed, utilizing deep learning techniques.
  • The method employs a sparse autoencoder with graph long short-term memory (SAE-GLSTM) for feature extraction from forged media.
  • Feature frames are extracted during the training phase using the SAE-GLSTM approach.

Main Results:

  • The proposed deepfake detection model was evaluated on diverse datasets including FFHQ, 100K-Faces, Celeb-DF (V2), and WildDeepfake.
  • The method demonstrated effectiveness in detecting computationally generated image and video spoofs.
  • Evaluated results confirm the efficacy of the developed deepfake detection approach.

Conclusions:

  • The developed SAE-GLSTM method offers a promising solution for detecting advanced deepfake manipulations.
  • This novel approach contributes to combating the spread of misinformation and enhancing digital privacy.
  • The study highlights the importance of continuous research in deepfake detection to counter emerging AI threats.