深度伪造与合成媒体：生成、检测与治理
Deepfakes and Synthetic Media: Generation, Detection, and Governance
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中文总结 AI 辅助
本文综述深度伪造的生成、检测与治理，涵盖GAN、扩散等生成模型及CNN、Transformer等检测方法，强调跨生成器泛化挑战，并提出整合取证、溯源与问责的纵深防御治理方案。
中文摘要 AI 辅助
深度伪造，即由深度生成模型产生的合成视听内容，已在民用和军事领域升级为一项关键威胁，导致身份欺诈、虚假信息宣传和证据伪造。在从新闻、金融到医疗和法律等高风险环境中，其后果延伸至严重的错误信息、市场操纵、身份欺诈以及机构信任的侵蚀。本条目探讨了现代视觉智能和计算机视觉技术如何用于检测深度伪造。它概述了关键的深度伪造生成模型，如GAN、自编码器、神经渲染和扩散系统，同时解释了对抗性方法如何增强真实感并挑战现有检测器。该综述强调了检测中常用的视觉伪影、数字模式和生理线索，并评述了主要的CNN、Transformer和基于频率的方法。它还总结了评估实践以及实现强泛化的难度。最后，它指出了新兴方向，包括用于民用和军事内容验证的现代智能技术。本综述涵盖了生成架构（GAN、潜在扩散、神经渲染、视频合成）、它们产生的空间、时间、频域和生理伪影，以及利用这些伪影的检测器家族。我们考察了评估基准和协议，强调跨生成器泛化是该领域的核心开放挑战。在检测之外，我们讨论了加密溯源标准、水印和监管框架（欧盟AI法案、DSA、GDPR）。我们得出结论，有效的深度伪造治理需要纵深防御，整合取证检测、可验证溯源和机构问责。
英文摘要
Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains, enabling identity fraud, disinformation campaigns, and evidence fabrication. In high-stakes environments, ranging from journalism and finance to healthcare and legal contexts, the consequences extend to severe misinformation, market manipulation, identity fraud, and the erosion of institutional trust. This entry explores how modern visual intelligence and computer vision techniques are used to detect deepfakes. It outlines key deepfake generation models, such as GANs, autoencoders, neural rendering, and diffusion systems, while also explaining how adversarial methods enhance realism and challenge existing detectors. The overview highlights visual artifacts, digital patterns, and physiological cues commonly leveraged in detection and reviews major CNN, transformer, and frequency-based approaches. It also summarizes evaluation practices and the difficulty of achieving strong generalization. Finally, it identifies emerging directions, including modern intelligence techniques for civilian and military content verification. This survey covers generation architectures (GANs, latent diffusion, neural rendering, video synthesis), the spatial, temporal, frequency-domain, and physiological artifacts they produce, and the detector families that exploit them. We examine evaluation benchmarks and protocols, highlighting cross-generator generalization as the field's central open challenge. Beyond detection, we discuss cryptographic provenance standards, watermarking, and regulatory frameworks (EU AI Act, DSA, GDPR). We conclude that effective deepfake governance requires defense-in-depth integrating forensic detection, verifiable provenance, and institutional accountability.
发表机构
- Heriot-Watt University（赫瑞-瓦特大学）
- Democritus University of Thrace（德谟克利特色雷斯大学）
- National and Technical University of Athens（雅典国立技术大学）
- Salvezza Energy Systems Ltd.（Salvezza能源系统有限公司）
- Hellenic Open University（希腊开放大学）
- Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
- Technical University of Sofia（索非亚技术大学）
- Hellenic Naval Academy（希腊海军学院）
机构由 AI 辅助整理，请以论文原文为准。
