Dynamic texture analysis for detecting fake faces in video sequences

TL;DR

This paper introduces a novel method using spatio-temporal texture analysis with Local Derivative Patterns on Three Orthogonal Planes (LDP-TOP) to effectively detect fake faces in videos, outperforming some deep models in efficiency.

Contribution

It proposes a new approach combining joint analysis of multiple video segments and texture dynamics across spatial and temporal dimensions for fake face detection.

Findings

LDP-TOP descriptors effectively distinguish real and fake videos.

The method identifies creation techniques used in manipulated videos.

Linear SVMs achieve performance comparable to deep learning models.

Abstract

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.