Abstract
With the boom of artificial intelligence, facial manipulation technology is becoming more simple and more numerous. At the same time, the technology also has a large and profound negative impact on face forensics, such as Deepfakes. In this paper, in order to aggregate multiframe features to detect facial manipulation videos, we solve facial manipulated video detection from set perspective and propose a novel framework based on set, which is called set convolutional neural network (SCNN). Three instances of the proposed framework SCNN are implemented and evaluated on the Deepfake TIMIT dataset, FaceForensics++ dataset and DFDC Preview datset. The results show that the method outperforms previous methods and can achieve state-of-the-art performance on both datasets. As a perspective, the proposed method is a fusion promotion of single-frame digital video forensics network.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Visual Communication and Image Representation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
