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 two worlds of content fingerprinting and 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 in a way similar to digital watermarking, thus allowing the extraction of fingerprints from the modified cover data. We consider several encoding strategies, examine the performance of the proposed schemes in terms of bit error rate, the probabilities of correct identification and false acceptance and compare it with those of conventional fingerprinting and digital watermarking. Finally, we extend the proposed framework to the multidimensional case based on lattices and demonstrate its performance on both synthetic data and real images.