深度人脸伪造与检测技术综述

谢天; 于灵云; 罗常伟; 谢洪涛; 张勇东

doi:10.16511/j.cnki.qhdxxb.2023.21.002

清华大学学报（自然科学版） >

2023 , Vol. 63 >Issue 9: 1350 - 1365

DOI: https://doi.org/10.16511/j.cnki.qhdxxb.2023.21.002

计算机科学与技术

深度人脸伪造与检测技术综述

谢天 ,
于灵云 ,
罗常伟 ,
谢洪涛 ,
张勇东

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1. 安徽大学安徽大学与合肥综合性国家科学中心人工智能研究院联合实验室, 合肥 230601;
2. 合肥综合性国家科学中心人工智能研究院, 合肥 230088;
3. 中国科学技术大学信息科学技术学院, 合肥 230027;
4. 清华大学电子工程系, 北京 100084;
5. 中国人民解放军军事科学院, 北京 100091

谢天(1999-),男,硕士研究生。

收稿日期: 2022-04-25

网络出版日期: 2023-08-19

基金资助

国家自然科学基金重点项目(62232006, U1936210); 国家自然科学基金青年科学基金项目(62102127)

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Survey of deep face manipulation and fake detection

XIE Tian ,
YU Lingyun ,
LUO Changwei ,
XIE Hongtao ,
ZHANG Yongdong

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1. AHU-IAI AI Joint Laboratory, Anhui University, Hefei 230601, China;
2. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China;
3. The School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China;
4. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;
5. Academy of Military Sciences, Beijing 100091, China

Received date: 2022-04-25

Online published: 2023-08-19

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摘要

由于现有深度人脸伪造软件通常是开源的或者被封装为APP免费分发, 导致了虚假视频的泛滥。因此开展针对深度人脸伪造检测技术的研究就显得尤为重要。目前, 深度人脸伪造及检测技术正处在快速发展时期, 各种相应的算法也在不断更新迭代。本文首先介绍了深度人脸伪造和其检测技术的代表性算法并给出简要分析, 其中深度人脸伪造技术包括身份替换、面部重演、属性编辑、人脸生成等, 检测技术包括图像级伪造检测技术和视频级伪造检测技术。然后归纳总结了常用的深度人脸伪造与检测数据集及不同算法的评估结果, 最后讨论了伪造技术及其检测技术目前面临的主要问题及发展方向。

关键词： 深度人脸伪造; 深度人脸伪造检测; 深度生成模型; 检测技术

本文引用格式

谢天 , 于灵云 , 罗常伟 , 谢洪涛 , 张勇东 . 深度人脸伪造与检测技术综述[J]. 清华大学学报（自然科学版）, 2023 , 63(9) : 1350 -1365 . DOI: 10.16511/j.cnki.qhdxxb.2023.21.002

Abstract

[Significance] Deep face manipulation technology involves the generation and manipulation of human imagery by different strategies, such as identity swapping or face reenactment between the source face and the target face. On the one hand, the rise of deep face manipulation has inspired a series of applications, including video making and advertising marketing. On the other hand, because face manipulation technology is usually open source or packaged as APPs for free distribution, it makes the threshold of tampering technology lower, resulting in the proliferation of fake videos. Moreover, when face manipulation technology is maliciously used by criminals to produce fake news, especially for important military and political officials, it will guide and intervene in public opinion, posing a great threat to national security and social stability. Therefore, the research on deep face forgery detection technology is particularly important. Hence, it is necessary to summarize the existing research to rationally guide deep face manipulation and detection technology.[Progress] Nowadays, deep face manipulation technology can be roughly divided into four types, namely, identity swapping, face reenactment, face editing, and face synthesis. Deepfakes bring real-world identity swapping to a new level of fidelity. The region-aware face-swapping network provides the identity information of source characters from local and global perspectives, making the generated faces more natural. In the field of facial reenactment, Wav2lip uses pretrained lip synchro models as expert models, encouraging the model to generate natural and accurate lip movements. In the field of face editing, FENeRF, a three-dimensional perception generator based on a neural radiation field, aligns semantic, geometric, and texture information in spatial domain and improves the consistency of the generated image between different perspectives while ensuring that the face can be edited. In the field of face synthesis, Anyface proposes a cross-modal distillation module for the alignment of language and visual representation, realizing the use of text information to generate more diversified face images. Deep face forgery detection technology can be roughly divided into image-level forgery detection and video-level forgery detection methods. In the image-level methods, SBI proposes a self-blended technique to generate realistic fake face images with data augmentation, effectively improving the generalization ability of the model. M2TR proposes a multimodal and multi-scale Transformer model to detect local artifacts at different levels of the image in spatial. Frequency domain features are also added as auxiliary information to ensure the forgery detection ability of the model for highly compressed images. In the video-level methods, RealForensics learns the natural correspondence between the face and audio in a real video in a self-supervised way, enhancing the generalization and robustness of the model.[Conclusions and Prospects] Presently, deep face manipulation and detection technologies are rapidly developing, and various corresponding technologies are in the process of continuous update and iteration. First, this survey reviews the deep face manipulation and detection methods and discusses their strengths and weaknesses. Second, the common datasets and the evaluation results of different manipulation and detection methods are summarized. Finally, the main challenges of face manipulation and fake detection are discussed, and the possible research direction in the future is pointed out.

Key words： deep face manipulation; deep face forgery detection; deep generative model; detection techniques

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