Exposing deepfakes in online communication: detection based on ensemble strategy
by Jie Xu; Guoqiang Wang; Tianxiong Zhou
International Journal of Autonomous and Adaptive Communications Systems (IJAACS), Vol. 17, No. 1, 2024

Abstract: In recent years, deepfake techniques have appeared in people's lives. As a product of deep learning, it can generate realistic face-swapping videos. Due to its high fidelity, deepfake is often used to produce porn videos and guide the public opinion, so as to pose a great threat to social stability. Previous studies have been able to improve detection accuracy. This paper aims to improve the detection ability of existing schemes by using the ensemble learning scheme from the perspective of model learning. Specifically, our scheme includes feature extraction, feature selection, feature classification, and a combination strategy. The experimental results on several datasets demonstrate that our scheme can effectively improve the detection ability of the model.

Online publication date: Wed, 10-Jan-2024

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