系统梳理音视频深度伪造检测技术与挑战
Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights
- 融合音视频双模态分析提升检测精度
- 总结主流方法优劣并指出研究空白
- 适合关注媒体安全与网络安全的研究者
深度学习在多个领域取得成功，其在深度伪造检测中的应用亦然。深度伪造是虚假但逼真的合成内容，可能被用于政治冒名、钓鱼攻击、诽谤或传播谣言。尽管已有大量关于单模态深度伪造检测的研究，但针对音视频联合分析以识别复杂伪造内容的工作仍相对不足。本文首先概述音视频深度伪造的生成技术、应用场景及其后果，随后全面回顾了当前结合音视频模态以提高检测准确率的先进方法，总结并批判性分析其优势与局限。此外，本文还讨论了现有的开源数据集，有助于研究社区深化理解，并为希望分析基于深度学习的音视频取证方法的初学者提供必要信息。通过弥合单模态与多模态方法之间的差距，本文旨在提升深度伪造检测策略的有效性，并指导未来在网络安全与媒体真实性领域的研究。
原文摘要 · Abstract (English)
Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To fill this gap, this survey first provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, and then provides a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy, summarizing and critically analyzing their strengths and limitations. Furthermore, we discuss existing open source datasets for a deeper understanding, which can contribute to the research community and provide necessary information to beginners who want to analyze deep learning-based audiovisual methods for video forensics. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity.
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