CASIA OpenIR  > 智能感知与计算研究中心
Bita-net: Bi-temporal attention network for facial video forgery detection
Ru, Yiwei; Zhou, Wanting; Liu, Yunfan; Sun, Jianxin; Li, Qi
Conference NameIEEE International Joint Conference on Biometrics
Conference Date2021-08
Conference PlaceChina

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.

Sub direction classification生物特征识别
planning direction of the national heavy laboratory视觉信息处理
Paper associated data
Document Type会议论文
Corresponding AuthorLi, Qi
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ru, Yiwei,Zhou, Wanting,Liu, Yunfan,et al. Bita-net: Bi-temporal attention network for facial video forgery detection[C],2021.
Files in This Item: Download All
File Name/Size DocType Version Access License
Bita-Net_Bi-temporal(1728KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ru, Yiwei]'s Articles
[Zhou, Wanting]'s Articles
[Liu, Yunfan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ru, Yiwei]'s Articles
[Zhou, Wanting]'s Articles
[Liu, Yunfan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ru, Yiwei]'s Articles
[Zhou, Wanting]'s Articles
[Liu, Yunfan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Bita-Net_Bi-temporal_Attention_Network_for_Facial_Video_Forgery_Detection.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.