CIF: Small: Theory and Algorithms for Statistical Content Identification

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Automatic Content Identification is an emerging technology that has found applications to broadcast monitoring, connected audio, content tracking, digital asset management, near-duplicate identification, contextual advertising, and as a filtering technology for file sharing. Content identification algorithms must be robust to common signal degradations. They operate on highly compressed data (robust hashes, aka content fingerprints) to meet storage, communication, and computing constraints.The goal of this project is to develop an analytical framework for content identification based upon fundamental principles and modern methods of statistical inference and information theory and to develop novel content identification algorithms. The project focuses on the following four research topics:1. Hash-Based Inference.2. Information-Theoretic Analysis: Content identification is formulated as a communication problem with storage constraints and its fundamental performance limits are investigated.3. Code Design: A learning-theoretic approach is developed for statistical modeling of content fingerprints and degradation channels from training data, and for designing hashing codes and decoding metrics that are optimally matched to these statistics.4. Applications: to audio, images, and video are explored, as well as forensic analysis and security.The project is synergistic and trains graduate students for leadership roles in information technology. The project benefits other areas, including but not limited to content retrieval, clustering, database indexing, pattern recognition, biometrics, and human/computer interaction.