Fast sequential forensic detection of camera fingerprint

Abstract

Two sequential camera source identification methods are proposed. Sequential tests implement a log-likelihood ratio test in an incremental way, thus enabling a reliable decision with a minimal number of observations. One of our methods adapts Goljan et al.'s to sequential operation. The second, which offers better performance in terms of error probabilities and average number of test observations, is based on treating the alternative hypothesis as a doubly stochastic model. We also discuss how the standard sequential test can be corrected to account for the event of weak fingerprints. Finally, we validate the goodness of our methods with experiments.

Improving PRNU Compression Through Preprocessing, Quantization, and Coding

“…Several sub-linear hashing methods based on reference fingerprint digest [32], [33] have been developed to address the problem of fast search in large dataset. Again in [34], the problem of minimizing the number of observations required to reduce error probabilities below some pre-fixed misdetection rates is addressed in a Sequential Probability Ratio Test framework. As of now, state-of-the-art in PRNU compression is achieved by binarized Gaussian Random Projections by Valsesia et al [35], [36].…”