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

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

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.