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
• DOI: 10.1109/TCSVT.2021.3133859
• ISSN: 1051-8215

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 the current forgery detection algorithms are based on convolutional neural networks to learn the feature differences between forged and real frames from big data. In this paper, from the perspective of image generation, we simulate the forgery process based on image generation and explore possible trace of forgery. We propose a multi-scale self-texture attention Generative Network(MSTA-Net) to track the potential texture trace in image generation process and eliminate the interference of deep forgery post-processing. Firstly, a generator with encoder-decoder is to disassemble images and performed trace generation, then we merge the generated trace image and the original map, which is input into the classifier with Resnet as the backbone. Secondly, the self-texture attention mechanism(STA) is proposed as the skip connection between the encoder and the decoder, which significantly enhances the texture characteristics in the image disassembly process and assists the generation of texture trace. Finally, we propose a loss function called Prob-tuple loss restricted by classification probability to amend the generation of forgery trace directly. To verify the performance of the MSTA-Net, we design different experiments to verify the feasibility and advancement of the method. Experimental results show that the proposed method performs well on deep forged databases represented by FaceForensics++, Celeb-DF, Deeperforensics and DFDC, and some results are reaching the state-of-the-art.