Bayesian/Learning Methods and Confidence Bounds for Signal Authentication, Traitor Tracing, and Forensics

数字媒体的丰富和编辑、重新格式化和操纵这些媒体的流行软件工具的可用性提出了真实性、信任和法医分析的基本问题。本项目探讨了这些问题的数学基础。应用领域包括信息保护和执法。该项目是高度跨学科的，涉及信号处理、通信、编码理论、信息论和学习理论的研究和教育活动之间的协同作用。具体来说，该项目开发了一个基于基本统计原理（贝叶斯和学习理论）的分析框架，以开发新的算法，并表征结果认证和取证分析的可靠性。该项目侧重于三个研究重点：1)去同步-弹性身份验证。在使用图形模型的无监督学习的最新进展的基础上，研究了去同步弹性的最终限制。2)法医分析。基于Vapnik-Chervonenkis理论，提出了图像感兴趣区域（ROI）识别的框架。3)盲目追踪。叛逆者追踪（又名数字指纹）的理论和代码是针对接收器无法获得要保护的原始信号的问题而开发的。

The abundance of digital media and the availability of popular software tools to edit, reformat, and manipulate these media has raised fundamental issues of authenticity, trust, and forensic analysis. This project exploresthe mathematical foundations for these problems. Applications areas include information protection and law enforcement. The project is highly multidisciplinary and involves a synergy between research and educational activities in signal processing, communications, coding theory, information theory, and learning theory.Specifically, the project develops an analytical framework based on fundamental statistical principles (Bayesian and learning theory) to develop novel algorithms and to characterize the reliability of the resulting authentication and forensic analyses. The project focuses on threeresearch thrusts:1) Desynchronization-Resilient Authentication. The ultimate limits of resilience to desynchronization are investigated, building on recent advances in unsupervised learning using graphical models. 2) Forensic Analysis. A framework is developed for Region of Interest (ROI) identification for images, based on Vapnik-Chervonenkis theory.3) Blind Traitor-Tracing. Theory and codes for traitor tracing (aka digital fingerprinting) are developed for problems where the original signal to be protected is not available to the receiver.