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Countering Malicious DeepFakes: Survey, Battleground, and Horizon.

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DeepFake technology creates realistic fake faces, leading to both beneficial and harmful uses. This study surveys DeepFake generation, detection, and evasion methods, offering insights into this rapidly evolving research area.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Digital Forensics

Background:

  • Deep generative approaches, termed DeepFake, enable sophisticated manipulation of facial appearance.
  • Applications range from visual effects in film to the malicious generation of misinformation.
  • The dual nature of DeepFake technology necessitates robust detection methods to identify synthetic media.

Purpose of the Study:

  • To provide a comprehensive overview and analysis of DeepFake generation, detection, and evasion research.
  • To clarify the 'battleground' dynamics between DeepFake creation and detection methodologies.
  • To identify research challenges, opportunities, trends, and future directions in the field.

Main Methods:

  • Systematic survey and analysis of over 318 research papers on DeepFake technology.
  • Development of a taxonomy for DeepFake generation techniques.
  • Categorization of DeepFake detection methods.
  • Showcasing the adversarial interactions between generation and detection.

Main Results:

  • Detailed taxonomy of DeepFake generation methods and categorization of detection approaches.
  • Illumination of the adversarial relationship, or 'battleground', between DeepFake creation and detection.
  • Identification of emerging trends, including DeepFake detection evasion strategies.

Conclusions:

  • The rapid advancement of DeepFake technology necessitates continuous research in detection and evasion.
  • Understanding the 'battleground' provides critical insights into the current landscape and future trajectory of DeepFake research.
  • Interactive diagrams are provided to facilitate exploration of popular DeepFake generators and detectors.