Fast sequential forensic camera identification

TL;DR

This paper introduces two sequential camera source identification methods that improve decision speed and accuracy by using incremental log-likelihood ratio tests, with validation through experiments.

Contribution

It presents novel sequential testing algorithms for forensic camera identification, including an adaptation of Goljan et al.'s method and a new doubly stochastic model approach.

Findings

The proposed methods achieve reliable decisions with fewer observations.

The new approach outperforms existing methods in error probabilities.

Experimental validation confirms effectiveness and robustness.

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.