期刊论文

Ding, Xiangling(xianglingding@163.com)

英文

Multimedia Tools and Applications

1380-7501

2023

82

10

14859-14877

10.1007/s11042-022-13966-x

This project is supported in part by the National Natural Science Foundation of China under grant 62172059 and 62072055, Hunan Provincial Natural Science Foundations of China under Grant 2020JJ4626 and 2020JJ4029, Scientific Research Fund of Hunan Provincial Education Department of China under Grant 19B004 and 16B004, the Opening Project of State Key Laboratory of Information Security under Grant 2021-ZD-07, the Opening Project of Guangdong Provincial Key Laboratory of Information Security Technology under Grant 2020B1212060078, Postgraduate Scientific Research Innovation Project of Changsha University of Science and Technology under Grant CX2021SS76, “Double First-class” International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology under Grant 2018IC25.

本校为第一机构

With the rapid development of Internet technology, the Internet is full of false information, and Deepfakes, as a kind of visual forgery content, brings the greatest impact to people. The existing mainstream Deepfakes public datasets often have millions of frames, and if the first N frames are used to train the model some key features may be lost. If all frames are used, the model is easily overfitted and training often takes several days, which greatly consumes computational resources. Therefore, we propose an adaptive video frame extraction a...