No abstract

No abstract

Photoresponse Non-Uniformity (PRNU) is becoming particularly relevant within digital media forensics, as a means to effectively determine the source camera of a given image. Most of the practical applications in digital media forensics involve dealing with highly sensitive data whose content must be protected. In this context, several secure frameworks have been proposed to perform PRNU-based camera attribution while preserving the privacy of both the testing images and the PRNU fingerprint. The two most recent and relevant ones, independently proposed in 2018, are (a) Mohanty et al.’s, who combine the use of a trusted environment (ARM TrustZone) to compute the PRNU fingerprint, with the Boneh-Goh-Nissim (BGN) cryptosystem to perform the matching, and (b) Pedrouzo-Ulloa et al.’s, who propose a more flexible solution which can be fully implemented on a general purpose architecture and does not require access to a trusted environment. In this work, we revisit the existing frameworks and propose a general formulation for PRNU matching based on lattice cryptosystems which improves on the BGN-based solution in terms of efficiency, flexibility and privacy.

No 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.