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Sensor fingerprint digests for fast camera identification from geometrically distorted images

In camera identification using sensor fingerprint, it is absolutely essential that the fingerprint and the noise residual from a given test image be synchronized. If the signals are desynchronized due to a geometrical transformation, fingerprint detection becomes significantly more complicated. Besides constructing the detector in an invariant transform domain (which limits the type of the geometrical transformation) a more general approach is to maximize the generalized likelihood ratio with respect to the transform parameters, which requires a potentially expensive search and numerous resamplings of the entire image (or fingerprint). In this paper, we propose a measure that significantly reduces the search complexity by reducing the need to resample the entire image to a much smaller subset of the signal called the fingerprint digest. The technique can be applied to an arbitrary geometrical distortion that does not involve spatial shifts, such as digital zoom and non-linear lens-distortion correction.