基于Transformer的人脸深度伪造检测技术综述

A Survey of Deepfake Detection Techniques Based on Transformer

人脸深度伪造检测旨在对人脸图像和视频进行真伪鉴别,能为肖像权保护、虚假消息鉴定、网络诈骗防范等提供理论和技术支撑。早期的检测技术主要基于卷积神经网络(Convolutional Neural Networks,CNNs)实现,并取得了显著的效果,但普遍存在泛化性能不足的问题。为了进一步提高人脸深度伪造检测技术的泛化性,最新的研究工作开始引入一种基于自我注意力机制的深度神经网络Transformer,其具有长距离依赖建模能力和全局感受野,可用于捕捉到图像上下文关联和视频时序关系,有效提高了检测器的表征能力。本综述首先简要介绍了该领域研究背景,阐述了人脸深度伪造生成典型技术,然后对现有基于Transformer的检测技术进行总结和归纳,最后探讨人脸深度伪造检测技术面临的挑战和未来研究方向。

Deepfake detection aims to authenticate facial images and videos,which can offer operational and technical support to safeguard personal portrait rights,prevent fake news,and curb online deceit.Early detection technologies are usually based on convolutional neural networks(CNNs)and have achieved promising detection performance.However,there exists a prevalent issue of mediocre generalisation performance.To enhance the overall generality of Deepfake detection,recent research has focused on a deep neural network Transformer by utilizing the self-attention mechanisms.The Transformer can better model long-distance dependency and global receptive fields to capture the image context association and video timing relationships,such that the representation ability of the detectors can be improved.This survey first provides an overview of the research background in this field,followed by an explanation of the common techniques used to generate Deepfake.Then,the existing Transformer-based detection methods are summarized and comparatively evaluated.Finally,the challenges and future research directions of Deepfake detection are discussed.