The creation of manipulated multimedia content involving human characters has reached in the last years unprecedented realism, calling for automated techniques to expose synthetically generated faces in images and videos.

This work explores the analysis of spatio-temporal texture dynamics of the video signal, with the goal of characterizing and distinguishing real and fake sequences. We propose to build a binary decision on the joint analysis of multiple temporal segments and, in contrast to previous approaches, to exploit the textural dynamics of both the spatial and temporal dimensions. This is achieved through the use of Local Derivative Patterns on Three Orthogonal Planes (LDP-TOP), a compact feature representation known to be an important asset for the detection of face spoofing attacks.

Experimental analyses on state-of-the-art datasets of manipulated videos show the discriminative power of such descriptors in separating real and fake sequences, and also identifying the creation method used. Linear Support Vector Machines (SVMs) are used which, despite the lower complexity, yield comparable performance to previously proposed deep models for fake content detection.

在过去几年中，涉及人物角色的受操纵多媒体内容的创建已经达到了前所未有的现实主义水平，需要自动化技术来在图像和视频中展示合成生成的人脸。

这项工作探索了对视频信号的时空纹理动态的分析，目的是表征和区分真假序列。我们建议在多个时间段的联合分析上建立一个二元决策，与以前的方法相比，利用空间和时间维度的纹理动态。这是通过使用三个正交平面上的局部衍生模式 (LDP-TOP) 来实现的，这是一种紧凑的特征表示，已知是检测人脸欺骗攻击的重要资产。

对最先进的操纵视频数据集的实验分析显示了此类描述符在分离真假序列以及识别所使用的创建方法方面的辨别力。使用线性支持向量机 (SVM)，尽管复杂度较低，但其性能与先前提出的用于虚假内容检测的深度模型相当。