This thesis is concerned with the theory and applications of an identification problem that arises in various multimedia management and security applications. In many of theese applications, data under analysis might be severely distorted. Consequently, an important issue of content identification systems is their ability to deal with distorted data. To address this issue, we introduce a new identification setup by using a fixed maximum list size decoder. In order to solve search and memory complexity issues in content identification with large-scale databases, we analyze a simple digital fingerprinting approach based on random projections. To address the search and memory complexity trade-off in identification systems, we introduce a decoding scheme capable of achieving the identification capacity. We introduce a database organization, based on assigning entries of a database to a set of overlapping clusters. We introduce a new framework called active content fingerprinting, which takes the best of content fingerprinting and digital watermarking to overcome some of the fundamental restrictions of these techniques in terms of performance and complexity.