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

丰富的数字媒体和流行的软件工具的可用性编辑，重新格式化和操纵这些媒体提出了真实性，信任和法医分析的基本问题。这个项目探索这些问题的数学基础。 应用领域包括信息保护和执法。 该项目是一个高度多学科的项目，涉及信号处理、通信、编码理论、信息理论和学习理论等领域的研究和教育活动的协同作用。具体而言，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，并表征由此产生的认证和法医分析的可靠性。该项目侧重于三个研究方向：1）去身份验证-弹性身份验证。的最终极限的弹性去冗余化的研究，建立在无监督学习使用图形模型的最新进展。2)法医分析。提出了一种基于Vapnik-Chervonenkis理论的图像感兴趣区域（ROI）识别框架。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.