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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Bibliographic Details

Published in:	2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE) pp. 1030 - 1034
Main Authors:	Hu, Tong, Liu, Wei, Fan, Yaqi, Yang, Jie, Qiao, Yuanyuan
Format:	Conference Proceeding
Language:	English
Published:	IEEE 10.01.2025
Subjects:	Attention mechanisms Autoencoders Costs Deep learning Deepfake detection Deepfakes Feature extraction Feature fusion Multimodal Robustness Scalability Security Training Unsupervised learning
Online Access:	Get full text
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