Content fingerprinting and digital watermarking are techniques that are used for content protection and distribution monitoring and, more recently, for interaction with physical objects. Over the past few years, both techniques have been well studied and their shortcomings understood. In this paper, we introduce a new framework called active content fingerprinting, which takes the best from these two worlds, i.e., the world of content fingerprinting and that of digital watermarking, in order to overcome some of the fundamental restrictions of these techniques in terms of performance and complexity. The proposed framework extends the encoding process of conventional content fingerprinting such that it becomes possible to extract more robust fingerprints from the modified data. We consider different encoding strategies and examine the performance of the proposed schemes in terms of content identification rate in an information theoretical framework and compare them with those of conventional content fingerprinting and watermarking.
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