Knowledge Commons of Institute of Automation,CAS
AUNet: Learning Relations Between Action Units for Face Forgery Detection | |
Bai WM(白炜铭)1,2; Liu YF(刘雨帆)1,2; Zhang ZP(张志鹏)3; Li B(李兵)1,4; Hu WM(胡卫明)1,2,5 | |
2023-06 | |
会议名称 | Conference on Computer Vision and Pattern Recognition |
会议日期 | 2023 年 6 月 18 日 – 2023 年 6 月 22 日 |
会议地点 | 加拿大温哥华温哥华会议中心 |
摘要 | Face forgery detection becomes increasingly crucial due to the serious security issues caused by face manipulation techniques. Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same domain. However, the problem remains challenging when one tries to generalize the detector to forgeries created by unseen methods during training. Observing that face manipulation may alter the relation between different facial action units (AU), we propose the Action-Units Relation Learning framework to improve the generality of forgery detection. In specific, it consists of the Action Units Relation Transformer (ART) and the Tampered AU Prediction (TAP). The ART constructs the relation between different AUs with AU-agnostic Branch and AU-specific Branch, which complement each other and work together to exploit forgery clues. In the Tampered AU Prediction, we tamper AU-related regions at the image level and develop challenging pseudo samples at the feature level. The model is then trained to predict the tampered AU regions with the generated location-specific supervision. Experimental results demonstrate that our method can achieve state-of-the-art performance in both the in-dataset and cross-dataset evaluations. |
七大方向——子方向分类 | 图像视频处理与分析 |
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/173211/56549 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
通讯作者 | Li B(李兵) |
作者单位 | 1.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.DiDiChuxing 4.People AI, Inc. 5.CAS Center for Excellence in Brain Science and Intelligence Technology |
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 | Bai WM,Liu YF,Zhang ZP,et al. AUNet: Learning Relations Between Action Units for Face Forgery Detection[C],2023. |
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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 | ||
camera_ready (5).pdf(5917KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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