Deepfake Video Detection Using 3D-Attentional Inception Convolutional Neural Network
Changlei Lu, Bin Liu, Wenbo Zhou, Qi Chu, Nenghai Yu
Abstract
The current spike of deepfake techniques has received considerable attention due to security concerns. To mitigate the potential risks brought by deepfake techniques, many detection methods have been proposed. However, most existing works merely leverage spatial information from separate frames and ignore valuable inter-frame temporal information. In this paper, we propose a deepfake detection scheme that uses 3D-attentional inception network. The proposed model encompasses both spatial and temporal information simultaneously with the 3D kernels. Furthermore, the channel and spatial-temporal attention modules are applied to improve detection capabilities. Comprehensive experiments demonstrate that our scheme outperforms state-of-the-art methods.