Dynamic texture analysis for detecting fake faces in video sequences

被引：0

作者：

Bonomi, Mattia ^{[1
]}

Pasquini, Cecilia ^{[1
,2
]}

Boato, Giulia ^{[1
]}

机构：

[1] Department of Computer Science and Information Engineering, University of Trento, Via Sommarive 9, Trento,38123, Italy

[2] Institut für Informatik, Universität Innsbruck, Technikerstraße 21A, Innsbruck,6020, Austria

来源：

Journal of Visual Communication and Image Representation | 2021年 / 79卷

关键词：

Face recognition - Support vector machines - Fake detection - Digital forensics;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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. © 2021 Elsevier Inc.

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