We provide Java codes for evaluating DC-QIM and RDM performance on anonymous fingerprinting schemes.
Fingerprinting is an essential tool to shun legal buyers of digital content from illegal redistribution. In fingerprinting schemes, the merchant embeds the buyer's identity as a watermark into the content, so that the merchant can retrieve the buyer's identity when he encounters a redistributed copy. To prevent the merchant from dishonestly embedding the buyer's identity multiple times, it is essential for the fingerprinting scheme to be anonymous. However, existing solution based on quantization index modulation (QIM) is fragile even against simple attacks like amplitude scaling.
In our work, we use robust watermarking techniques within the anonymous finger-printing approach. We show that distortion compensated QIM (DC-QIM) and rational dither modulation (RDM) can be adapted to work in encrypted domain thus the robustness of the embedded fingerprints can be improved.
We provide a class called Connector in Java codes that we used for validation of this work (Eurasip Journal on Information Security, 2008).