Blind Fingerprinting
  We study blind fingerprinting, where the host sequence into which fingerprints are embedded is partially or completely unknown to the decoder. This problem relates to a multiuser version of the Gel'fand-Pinsker problem. The number of colluders and the collusion channel are unknown, and the colluders and the fingerprint embedder are subject to distortion constraints.   We propose a conditionally constant-composition random binning scheme and a universal decoding rule and derive the corresponding false-positive and false-negative error exponents. The encoder is a stacked binning scheme and makes use of an auxiliary random sequence. The decoder is a {\em maximum doubly-penalized mutual information decoder}, where the significance of each candidate coalition is assessed relative to a threshold that trades off false-positive and false-negative error exponents. The penalty is proportional to coalition size and is a function of the conditional type of host sequence. Positive exponents are obtained at all rates below a certain value, which is therefore a lower bound on public fingerprinting capacity. We conjecture that this value is the public fingerprinting capacity. A simpler threshold decoder is also given, which has similar universality properties but also lower achievable rates. An upper bound on public fingerprinting capacity is also derived. 
Ying Wang and Pierre Moulin