Fast sequential forensic camera identification
This work addresses the need for efficient and accurate camera source identification in forensic applications, representing an incremental improvement over prior methods.
The authors tackled the problem of forensic camera identification by proposing two sequential methods that enable reliable decisions with minimal observations, achieving better performance in terms of error probabilities and average test observations compared to an adapted existing method.
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.