An Attention Fusion-Based Multimodal Autoencoder Deepfake Detection Framework

Deepfake detection is a critical task to mitigate the risks posed by AI-generated fake media content across societal, political, and security domains. As the quality of generated content improves, traditional single-modal detection methods increasingly struggle to address the complexity and diversit...

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Veröffentlicht in:	2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE) S. 1030 - 1034
Hauptverfasser:	Hu, Tong, Liu, Wei, Fan, Yaqi, Yang, Jie, Qiao, Yuanyuan
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 10.01.2025
Schlagworte:	Attention mechanisms Autoencoders Costs Deep learning Deepfake detection Deepfakes Feature extraction Feature fusion Multimodal Robustness Scalability Security Training Unsupervised learning
Online-Zugang:	Volltext
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