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Related Experiment Video

Updated: Sep 26, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Deepfake Video Detection Based on EfficientNet-V2 Network.

Liwei Deng1, Hongfei Suo1, Dongjie Li1

  • 1Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin 150080, China.

Computational Intelligence and Neuroscience
|April 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces EfficientNet-V2 for detecting fake images and videos, enhancing societal stability by combating deepfake misinformation. The new network significantly improves accuracy in distinguishing real from manipulated media.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Deep learning advancements enable sophisticated fake media creation.
  • Malicious use of deepfakes (e.g., political disinformation, financial fraud) poses societal risks.
  • Existing fake face detection systems require enhanced performance.

Purpose of the Study:

  • To develop an advanced deepfake detection system.
  • To leverage EfficientNet-V2 for improved authenticity verification of images and videos.
  • To mitigate the societal impact of sophisticated fake media.

Main Methods:

  • Implementation of the EfficientNet-V2 network for deepfake detection.
  • Training and testing the model on two large-scale, mainstream fake face datasets.
  • Comparative analysis against existing deepfake detection networks.

Main Results:

  • EfficientNet-V2 demonstrated superior performance compared to existing detection networks.
  • The proposed method achieved high accuracy in distinguishing real from fake faces.
  • Successful detection of real-world images and videos with excellent visualization.

Conclusions:

  • EfficientNet-V2 is a highly effective architecture for deepfake detection.
  • The enhanced detection system contributes to combating the spread of misinformation.
  • The study provides a robust solution for verifying media authenticity.