Deepfake detection based on video flow spectrum feature space
The rapid advancement of deepfake technology has led to the creation of deepfake videos that appear extremely realistic on each frame.Existing detection methods have struggled to effectively identify deepfake videos.To tackle this issue,a deepfake detection method based on video flow spectrum feature space is proposed for the first time in this paper.The video flow spectrum feature space is constructed using flow spectrum theory,which maps the spatio-temporal information in the video to the video flow spectrum feature space through the video flow spectrum basis model.This approach better captures the motion inconsistency of the video and obtains a more discriminative video representation,enabling the detection of deepfake videos.Specifically,the paper proposes a method for constructing the video flow spectrum feature space,which obtains an approximately isomorphic video flow spectrum feature description space by basis-mapping the video feature hidden space.It also fuses the high-dimensional representations of different perspectives of the video stream in the video flow spectrum feature space to achieve accurate portrayal and analysis of video streams.Furthermore,a video inconsistency flow spectrum map model is designed to map the spatial information of the video stream into the video flow spectrum feature space from the temporal perspective using the video flow spectrum transform operator.This model effectively captures the inconsistency information of deepfake videos and constructs the video representation with higher data separability.Experimental results demonstrate that the proposed method achieves an accuracy of 99.23％on the Celeb-DF dataset and 95.24％on the DFDC dataset.
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