Statistical Inference Methods and Confidence Bounds for Signal Authentication and Traitor Tracing

该项目解决了数字媒体真实性和信任领域的一些基本科学问题。这些问题出现在诸如伪造检测和表征、用于内容保护的数字指纹和交易跟踪等应用中。该项目开发了一个分析框架，用于解决基于基本原理和现代统计推断方法的挑战性问题;开发了新的算法;并评估接收者决定的可靠性。该项目的教育部分包括一个针对高中生和本科生的暑期研究计划，向他们传授有关信息数字版权管理的伦理和技术。研究部分该项目的重点是以下两个方面。第一，去干扰弹性身份验证，利用贝叶斯递归过滤和使用图形模型的推理的最新进展。 第二，盲指纹识别（或叛逆者追踪），为接收器无法获得原始信号的问题开发理论和代码。

This project addresses some fundamental scientific questions in the areas of authenticity and trust for digital media. Such issues arise in applications such as forgery detection and characterization, digital fingerprinting for content protection, and transaction tracking. This project develops an analytical framework for solving challenging problems based upon fundamental principles and modern methods of statistical inference; develops novel algorithms; and assesses the reliability of the receiver's decisions.The educational component of this project includes a summer research program for high school students and undergraduates that teaches them about the ethics and technology surrounding information digital rights management.The research component of the project focuses on the following two thrusts. First, desynchronization-resilient authentication, exploiting recent advances in Bayesian recursive filtering and inference using graphical models. Second, blind fingerprinting (or traitor tracing), developing theory and codes for problems where the original signal is not available to the receiver.