论文标题
DeepFakeson-Phys：基于心率估计的DeepFakes检测
DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation
论文作者
论文摘要
这项工作引入了基于生理测量的新型深泡检测框架。特别是，我们考虑使用远程光绘画学（RPPG）与心率相关的信息。 RPPG方法分析视频序列，以寻找人类皮肤中的微妙颜色变化，从而揭示了组织下的人体血液的存在。在这项工作中，我们调查了RPPG在多大程度上可用于检测深击视频。 提出的名为DeepFakeson-Phys的伪造探测器使用卷积注意网络（CAN），该网络从视频框架中提取空间和时间信息，分析和组合两个源以更好地检测假视频。这种检测方法已使用该领域中最新的公共数据库进行了实验评估：Celeb-DF和DFDC。结果达到了两个数据库上98％的AUC（曲线下的区域），其表现优于最新的状态，并证明了基于生理测量的假探测器的成功，以检测最新的DeepFake视频。
This work introduces a novel DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. In this work we investigate to what extent rPPG is useful for the detection of DeepFake videos. The proposed fake detector named DeepFakesON-Phys uses a Convolutional Attention Network (CAN), which extracts spatial and temporal information from video frames, analyzing and combining both sources to better detect fake videos. This detection approach has been experimentally evaluated using the latest public databases in the field: Celeb-DF and DFDC. The results achieved, above 98% AUC (Area Under the Curve) on both databases, outperform the state of the art and prove the success of fake detectors based on physiological measurement to detect the latest DeepFake videos.
