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Deepfake Detection Using the Rate of Change between Frames Based on Computer Vision.

Gihun Lee1, Mihui Kim1

  • 1Department of Computer Science & Engineering, Computer System Institute, Hankyong National University, Jungang-ro, Anseong-si 17579, Gyeonggi-do, Korea.

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Summary
This summary is machine-generated.

This study introduces a novel method for detecting deepfakes by analyzing computer vision features in digital videos. The technique achieves high detection rates, outperforming existing approaches in identifying manipulated content.

Keywords:
computer visiondeepfakethe rate of change

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Digital Forensics

Background:

  • Artificial intelligence (AI) advancements have led to sophisticated applications but also ethical challenges like deepfakes.
  • Deepfakes, AI-generated fake videos, pose significant risks including political manipulation, misinformation, and non-consensual pornography.
  • Existing detection methods struggle against advanced manipulation techniques.

Purpose of the Study:

  • To propose and evaluate a novel method for detecting manipulated digital video content (deepfakes).
  • To enhance the integrity verification of digital media in the face of AI-driven forgery.

Main Methods:

  • Analyzing computer vision features within digital video content.
  • Extracting the rate of change in computer vision features between adjacent video frames.
  • Assessing video manipulation by monitoring feature changes.

Main Results:

  • The proposed method achieved a 97% detection rate, surpassing existing and machine learning techniques.
  • Demonstrated robust performance with a 96% detection rate even against image matrix manipulation designed to evade convolutional neural network detection.

Conclusions:

  • The developed computer vision-based method offers a highly effective solution for deepfake detection.
  • This approach provides a reliable tool for verifying the integrity of digital content against sophisticated AI manipulations.