Inconsistency-Aware Wavelet Dual-Branch Network for Face Forgery Detection | |
jia geng yun1,2; zheng mei song4; hu chuan rui4; ma xin1,2; xu yu ting1,2; liu luo qi4; deng ya feng4; he ran1,2,3 | |
发表期刊 | IEEE Transactions on Biometrics, Behavior, and Identity Science |
2021-06-07 | |
卷号 | 3期号:3页码:308-319 |
摘要 | Current face forgery techniques can generate high-fidelity fake faces with extremely low labor and time costs. As a result, face forgery detection becomes an important research topic to prevent technology abuse. In this paper, we present an inconsistency-aware wavelet dual-branch network for face forgery detection. This model is mainly based on two kinds of forgery clues called inter-image and intra-image inconsistencies. To fully utilize them, we firstly enhance the forgery features by using additional inputs based on stationary wavelet decomposition (SWD). Then, considering the different properties of the two inconsistencies, we design a dual-branch network that predicts image-level and pixel-level forgery labels respectively. The segmentation branch aims to recognize real and fake local regions, which is crucial for discovering intra-image inconsistency. The classification branch learns to discriminate the real and fake images globally, thus can extract inter-image inconsistency. Finally, bilinear pooling is employed to fuse the features from the two branches. We find that the bilinear pooling is a kind of spatial attentive pooling. It effectively utilizes the rich spatial features learned by the segmentation branch. Experimental results show that the proposed method surpasses the state-of-the-art face forgery detection methods. |
语种 | 英语 |
资助项目 | Beijing Natural Science Foundation[JQ18017] |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48594 |
专题 | 智能感知与计算研究中心 |
通讯作者 | jia geng yun |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.National Laboratory of Pattern Recognition, Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences 3.CAS Center for Excellence in Brain Science and Intelligence Technology 4.360 AI Institute, Beijing Qihu Keji Company Ltd |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | jia geng yun,zheng mei song,hu chuan rui,et al. Inconsistency-Aware Wavelet Dual-Branch Network for Face Forgery Detection[J]. IEEE Transactions on Biometrics, Behavior, and Identity Science,2021,3(3):308-319. |
APA | jia geng yun.,zheng mei song.,hu chuan rui.,ma xin.,xu yu ting.,...&he ran.(2021).Inconsistency-Aware Wavelet Dual-Branch Network for Face Forgery Detection.IEEE Transactions on Biometrics, Behavior, and Identity Science,3(3),308-319. |
MLA | jia geng yun,et al."Inconsistency-Aware Wavelet Dual-Branch Network for Face Forgery Detection".IEEE Transactions on Biometrics, Behavior, and Identity Science 3.3(2021):308-319. |
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