Detecting Compressed Deepfake Videos in Social Networks Using Frame-Temporality Two-Stream Convolutional Network
文献类型：期刊论文
| 作者 | Hu, Juan3; Liao, Xin2,3; Wang, Wei1 ; Qin, Zheng3
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| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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| 出版日期 | 2022-03-01 |
| 卷号 | 32期号:3页码:1089-1102 |
| 关键词 | Videos Information integrity Feature extraction Streaming media Faces Forensics Social networking (online) Video forensics compressed Deepfake videos frame-level stream temporality-level stream |
| ISSN号 | 1051-8215 |
| DOI | 10.1109/TCSVT.2021.3074259 |
| 通讯作者 | Liao, Xin(xinliao@hnu.edu.cn) |
| 英文摘要 | The development of technologies that can generate Deepfake videos is expanding rapidly. These videos are easily synthesized without leaving obvious traces of manipulation. Though forensically detection in high-definition video datasets has achieved remarkable results, the forensics of compressed videos is worth further exploring. In fact, compressed videos are common in social networks, such as videos from Instagram, Wechat, and Tiktok. Therefore, how to identify compressed Deepfake videos becomes a fundamental issue. In this paper, we propose a two-stream method by analyzing the frame-level and temporality-level of compressed Deepfake videos. Since the video compression brings lots of redundant information to frames, the proposed frame-level stream gradually prunes the network to prevent the model from fitting the compression noise. Aiming at the problem that the temporal consistency in Deepfake videos might be ignored, we apply a temporality-level stream to extract temporal correlation features. When combined with scores from the two streams, our proposed method performs better than the state-of-the-art methods in compressed Deepfake videos detection. |
| WOS关键词 | FORENSICS |
| 资助项目 | National Natural Science Foundation of China[61972142] ; National Natural Science Foundation of China[61972395] ; National Natural Science Foundation of China[61772191] ; Hunan Provincial Natural Science Foundation of China[2020JJ4212] ; Key Lab of Information Network Security, Ministry of Public Security[C20611] ; Science and Technology Program of Changsha[kq2004021] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000766700400017 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Hunan Provincial Natural Science Foundation of China ; Key Lab of Information Network Security, Ministry of Public Security ; Science and Technology Program of Changsha |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48087]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Liao, Xin |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing 100093, Peoples R China 3.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Juan,Liao, Xin,Wang, Wei,et al. Detecting Compressed Deepfake Videos in Social Networks Using Frame-Temporality Two-Stream Convolutional Network[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(3):1089-1102. |
| APA | Hu, Juan,Liao, Xin,Wang, Wei,&Qin, Zheng.(2022).Detecting Compressed Deepfake Videos in Social Networks Using Frame-Temporality Two-Stream Convolutional Network.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(3),1089-1102. |
| MLA | Hu, Juan,et al."Detecting Compressed Deepfake Videos in Social Networks Using Frame-Temporality Two-Stream Convolutional Network".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.3(2022):1089-1102. |
入库方式： OAI收割
来源：自动化研究所
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