Research on Self-Evolving Deepfake Detection Technology Based on LLM

Deepfake technology enables the manipulation of text, images, audio, and video, offering innovative applications in areas like virtual reality and entertainment. However, it also poses risks such as fake news, privacy breaches, and political misuse, challenging existing static detection models that struggle to adapt to evolving manipulation techniques. This study proposes a self-evolving deepfake detection system using generative AI (LLM) to learn and adapt in real time. The framework integrates real-time learning, multimodal data processing, and lightweight models, emphasizing ethical data practices to ensure fairness, transparency, and security. By addressing technical, social, and legal challenges, this research provides a robust solution for mitigating deepfake risks while fostering ethical and reliable AI deployment.

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Publication Details

Published
2025-03-29
DOI
https://doi.org/10.1109/icct-pacific63901.2025.11012805
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Research on Self-Evolving Deepfake Detection Technology Based on LLM

Cheolhee Yoon, Junghyun Cho, Kang Jang Mook
Anomaly Detection Techniques and Applications
article

Research on Self-Evolving Deepfake Detection Technology Based on LLM

Cheolhee Yoon, Junghyun Cho, Kang Jang Mook
article en

Abstract

Deepfake technology enables the manipulation of text, images, audio, and video, offering innovative applications in areas like virtual reality and entertainment. However, it also poses risks such as fake news, privacy breaches, and political misuse, challenging existing static detection models that struggle to adapt to evolving manipulation techniques. This study proposes a self-evolving deepfake detection system using generative AI (LLM) to learn and adapt in real time. The framework integrates real-time learning, multimodal data processing, and lightweight models, emphasizing ethical data practices to ensure fairness, transparency, and security. By addressing technical, social, and legal challenges, this research provides a robust solution for mitigating deepfake risks while fostering ethical and reliable AI deployment.

Korean National Police University (KR), Far East University (KR)
Openalex Percentile: Top 94%
Anomaly Detection Techniques and Applications
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