  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.
