使用几何伪造特征的多人脸视频Deepfake检测
Deepfake detection in videos with multiple faces using geometric-fakeness features
AI总结：
针对多人脸视频中Deepfake检测性能下降问题，提出几何伪造特征（GFF）表征人脸动态存在度与逐帧得分，并训练深度学习模型分析时间不一致性，在多个基准数据集上优于现有方法。
摘要：
近年来，随着面部操纵技术的发展，视频流中的Deepfake检测已成为面部生物特征识别、品牌监控或在线视频会议解决方案的重要问题。在生物特征认证中，如果用Deepfake替换真实数据流，就可以绕过活体检测系统。在视频会议中使用Deepfake，可以潜入私人会议。受害者或公众人物的Deepfake也可能被欺诈者用于敲诈勒索和金融诈骗。因此，检测Deepfake的任务与确保隐私和安全密切相关。在现有的Deepfake检测方法中，当视频同时存在多张人脸或有其他物体被误分类为人脸时，其性能会下降。在我们的研究中，我们提出使用几何伪造特征（GFF）来表征视频中人脸存在的动态程度及其逐帧Deepfake得分。为了分析帧间GFF的时间不一致性，我们训练了一个复杂的深度学习模型来输出最终的Deepfake预测。我们采用我们的方法来分析视频中同时存在多张人脸的视频。这类视频在实践中经常出现，例如在线视频会议。在这种情况下，与Deepfake人脸同时出现在一帧中的真实人脸会显著影响Deepfake检测，而我们的方法可以解决这个问题。通过大量实验，我们证明我们的方法在FaceForensics++、DFDC、Celeb-DF和WildDeepFake等流行基准数据集上优于当前最先进的方法。当训练用于检测多种不同的Deepfake生成技术时，所提出的方法仍保持准确。
英文摘要：
Due to the development of facial manipulation techniques in recent years deepfake detection in video stream became an important problem for face biometrics, brand monitoring or online video conferencing solutions. In case of a biometric authentication, if you replace a real datastream with a deepfake, you can bypass a liveness detection system. Using a deepfake in a video conference, you can penetrate into a private meeting. Deepfakes of victims or public figures can also be used by fraudsters for blackmailing, extorsion and financial fraud. Therefore, the task of detecting deepfakes is relevant to ensuring privacy and security. In existing approaches to a deepfake detection their performance deteriorates when multiple faces are present in a video simultaneously or when there are other objects erroneously classified as faces. In our research we propose to use geometric-fakeness features (GFF) that characterize a dynamic degree of a face presence in a video and its per-frame deepfake scores. To analyze temporal inconsistencies in GFFs between the frames we train a complex deep learning model that outputs a final deepfake prediction. We employ our approach to analyze videos with multiple faces that are simultaneously present in a video. Such videos often occur in practice e.g., in an online video conference. In this case, real faces appearing in a frame together with a deepfake face will significantly affect a deepfake detection and our approach allows to counter this problem. Through extensive experiments we demonstrate that our approach outperforms current state-of-the-art methods on popular benchmark datasets such as FaceForensics++, DFDC, Celeb-DF and WildDeepFake. The proposed approach remains accurate when trained to detect multiple different deepfake generation techniques.
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