MSTA-Net: Forgery Detection by Generating Manipulation Trace Based on Multi-Scale Self-Texture Attention

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 7; pp. 4854 - 4866
• Main Authors: Yang, Jiachen, Xiao, Shuai, Li, Aiyun, Lu, Wen, Gao, Xinbo, Li, Yang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Coders; Dismantling; Encoders-Decoders; Faces; faceswap detection; Feature extraction; Forgery; Generators; Image processing; Information integrity; prob-tuple loss; self-texture attention; Texture; Trace generation; Videos

Lots of Deepfake videos are circulating on the Internet, which not only damages the personal rights of the forged individual, but also pollutes the web environment. What's worse, it may trigger public opinion and endanger national security. Therefore, it is urgent to fight deep forgery. Most of...