We provide background on emerging challenges and future directions with media integrity and authentication methods, focusing on distinguishing AI-generated media from authentic content captured by cameras and microphones. We evaluate several approaches, including provenance, watermarking, and fingerprinting. After defining each method, we analyze three representative technologies: cryptographically secured provenance, imperceptible watermarking, and soft-hash fingerprinting. We analyze how these tools operate across modalities and evaluate relevant threat models, attack categories, and real-world workflows spanning capture, editing, distribution, and verification. We consider sociotechnical reversal attacks that can invert integrity signals, making authentic content appear synthetic and vice versa, highlighting the value of verification systems that are resilient to both technical and psychosocial manipulation. Finally, we outline techniques for delivering high-confidence provenance authentication, including directions for strengthening edge-device security using secure enclaves.

翻译：本文针对媒体完整性与认证方法的新兴挑战及未来方向提供背景分析，重点探讨如何区分AI生成媒体与摄像机和麦克风采集的真实内容。我们评估了包括溯源、数字水印和指纹识别在内的多种技术路径。在界定各类方法后，我们重点分析三项代表性技术：加密安全溯源、不可感知水印和软哈希指纹识别。通过考察这些工具在多模态场景下的运作机制，我们评估了相关威胁模型、攻击分类以及涵盖采集、编辑、分发、验证环节的实际工作流程。特别关注能够逆转完整性信号的社会技术逆向攻击——此类攻击可使真实内容呈现合成特征，反之亦然——从而凸显对技术操纵和心理社会操纵均具鲁棒性的验证系统的价值。最后，我们概述了实现高可信度溯源认证的技术路径，包括利用安全飞地强化边缘设备安全性的发展方向。