Bita-net: Bi-temporal attention network for facial video forgery detection | |
Ru, Yiwei; Zhou, Wanting![]() ![]() ![]() | |
2021-07 | |
会议名称 | IEEE International Joint Conference on Biometrics |
会议日期 | 2021-08 |
会议地点 | China |
摘要 | Deep forgery detection on video data has attracted remarkable research attention in recent years due to its potential in defending forgery attacks. However, existing methods either only focus on the visual evidence within individual images, or are too sensitive to fluctuations across frames. To address these issues, this paper propose a novel model, named Bita-Net, to detect forgery faces in video data. The network design of Bita-Net is inspired by the mechanism of how human beings detect forgery data, i.e. browsing and scrutinizing, which is reflected by the two-pathway architecture of Bita-Net. Concretely, the browsing pathway scans the entire video at a high frame rate to check the temporal consistency, while the scrutinizing pathway focuses on analyzing key frames of the video at a lower frame rate. Furthermore, an attention branch is introduced to improve the forgery detection ability of the scrutinizing pathway. Extensive experiment results demonstrate the effectiveness and generalization ability of Bita-Net on various popular face forensics detection datasets, including FaceForensics++, CelebDF, DeepfakeTIMIT and UADFV. |
七大方向——子方向分类 | 生物特征识别 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/55260 |
专题 | 模式识别实验室 |
通讯作者 | Li, Qi |
作者单位 | Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Ru, Yiwei,Zhou, Wanting,Liu, Yunfan,et al. Bita-net: Bi-temporal attention network for facial video forgery detection[C],2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Bita-Net_Bi-temporal(1728KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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