视频深度伪造检测的泛化性问题：方法、挑战与技术进展

李俊杰; 王健宗; 张旭龙; 瞿晓阳

doi:10.11959/j.issn.2096-0271.2025072

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视频深度伪造检测的泛化性问题：方法、挑战与技术进展

更新时间：2026-02-27

- 视频深度伪造检测的泛化性问题：方法、挑战与技术进展
- Generalization challenges in video deepfake detection: methods, obstacles, and technological advances
- 大数据 2026年页码：1-20
- 作者机构：
  
  1.平安科技（深圳）有限公司，广东深圳 518063
  2.华中科技大学，湖北武汉 430070
- 作者简介：
  
  [ "李俊杰（2002‒），男，华中科技大学硕士生，平安科技（深圳）有限公司实习生，主要研究方向为视频深度伪造检测、分布外样本检测等。" ]
  [ "王健宗（1983‒），男，博士，平安科技（深圳）有限公司副总工程师，资深人工智能总监，联邦学习技术部总经理，智能金融前沿技术研究院院长。美国佛罗里达大学人工智能博士后，美国莱斯大学和华中科技大学联合培养博士，中国计算机学会资深会员，中国计算机学会大数据专家委员会委员，中国自动化学会联邦数据和联邦智能专业委员会副主任。主要研究方向为大模型、联邦学习和深度学习等。" ]
  [ "张旭龙（1988‒），男，博士，平安科技（深圳）有限公司，高级算法研究员，复旦大学计算机理学博士，2023年入选上海市东方英才计划青年项目。兼任深圳清华大学研究院及中国科学技术大学先进技术研究院校外导师，中国自动化学会联邦数据与联邦智能专业委员会委员。目前是IEEE、中国自动化学会以及中国计算机学会会员。主要研究方向为大模型、具身智能、跨模态智能计算以及模型上下文协议（MCP）等。发表学术论文80余篇，申请国家发明专利授权20余项。" ]
  [ "瞿晓阳（1988-），男，博士，平安科技（深圳）有限公司前沿机器学习算法分组分责人，清华大学深圳国际研究生院校外导师，中国科技大学校先进技术研究院校外导师，中佛罗里达大学访问学者，从事机器学习、大数据、体系结构方面的研究工作，在语音语义分析、自动化机器学习、零样本和小样本学习、高性能计算与存储等方面经验丰富。在体系结构方向（如INFOCOM、DAC、IPDPS、TPDS）和人工智能方向（如NeurIPS、IJCAI、ICASSP、Interspeech）等国际会议和期刊发表过近50篇文章，其中1篇论文荣获会议最佳学生论文奖提名；担任多个国际期刊的评委，已授权专利70篇，已出版专著2本。" ]
- 基金信息：
  
  广东省重点领域研发计划“新一代人工智能”重大专项;The Key Research and Development Program of Guangdong Province(2021B0101400003)
- DOI：10.11959/j.issn.2096-0271.2025072
  中图分类号： TP181
- 收稿：2025-03-31，
  
  网络首发：2026-02-25，
- 稿件说明：
移动端阅览
李俊杰,王健宗,张旭龙等.视频深度伪造检测的泛化性问题：方法、挑战与技术进展[J].大数据,

LI Junjie,WANG Jianzong,ZHANG Xulong,et al.Generalization challenges in video deepfake detection: methods, obstacles, and technological advances[J].BIG DATA RESEARCH,
李俊杰,王健宗,张旭龙等.视频深度伪造检测的泛化性问题：方法、挑战与技术进展[J].大数据, DOI：10.11959/j.issn.2096-0271.2025072.

LI Junjie,WANG Jianzong,ZHANG Xulong,et al.Generalization challenges in video deepfake detection: methods, obstacles, and technological advances[J].BIG DATA RESEARCH, DOI：10.11959/j.issn.2096-0271.2025072.

摘要

随着人工智能技术的快速发展，深度伪造技术已成为生成逼真人类音频、图像和视频的强大工具，其普及与成本降低对个人隐私和社会信任构成了严峻威胁。综述了视频深度伪造检测领域的泛化性问题，系统回顾了技术方法、挑战与进展。首先梳理了深度伪造检测技术的发展脉络，并对神经网络与人类在检测任务中的表现进行了对比分析。针对跨数据集检测的挑战，分析了由于数据集间特征分布差异，传统深度学习模型在跨域应用中的性能下降问题。为解决这一问题，研究者提出了多种方法，如神经网络架构设计、多目标学习、新增损失函数、数据增强、创新训练流程和生物特征融合技术等。近年来，随着检测技术进入稳定发展阶段，研究逐渐遭遇瓶颈，亟须突破。系统分析该领域的最新成果，剖析技术优势与不足，揭示了在提升检测泛化性及应对未知分布域方面的潜力与挑战，为后续研究提供参考。

Abstract

With the rapid development of artificial intelligence

deepfake technology has become a powerful tool for generating realistic audio

images

and videos. However

its widespread use and decreasing costs pose serious threats to personal privacy and social trust. This paper reviews the generalization issues in the field of video deepfake detection

providing a comprehensive overview of methods

challenges

and advancements. It first traces the development of deepfake detection techniques and compares the performance of neural networks with human capabilities in detection tasks. Regarding the challenges of cross-dataset detection

the paper analyzes how differences in feature distributions between datasets lead to the performance degradation of traditional deep learning models in cross-domain applications. To address this issue

researchers have proposed various solutions

such as neural network architecture design

multi-task learning

new loss functions

data augmentation

innovative training procedures

and the integration of biometric recognition technologies. In recent years

as deepfake detection research has reached a stage of stable development

it has also encountered bottlenecks

calling for breakthroughs. Through a systematic analysis of the latest research achievements in this field

this paper examines the strengths and limitations of current techniques

revealing both the potential and challenges in enhancing generalization and addressing unknown distribution domains. The insights provided offer valuable guidance for future research and applications.

关键词

Keywords

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