Efficient PRNU Matching in the Encrypted Domain

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.

A Review of “Camera Attribution Forensic Analyzer in the Encrypted Domain”

“…As we discussed in [6], although Mohanty et al's scheme evaluates most of the computation in the clear, their runtimes do not improve those obtained by our solution. In fact, the PRNU matching in their scheme could be more efficiently calculated by substituting the BGN cryptosystem with more modern lattice-based cryptosystems.…”

“…To the best of our knowledge [6], there are two different approaches for secure camera attribution: (a) Mohanty et al, [7], [8] who combine a trusted environment (ARM TrustZone) for the computation of the PRNU fingerprint, with the Boneh-Goh-Nissim (BGN) cryptosystem for the matching, and (b) ours [1], which proposes a more flexible solution that can be implemented on a general purpose architecture and does not require access to a trusted environment.…”

SSS-PRNU: Privacy-Preserving PRNU Based Camera Attribution using Shamir Secret Sharing

“…Dirik et al [3] proved that the method is inefficient if the uncarved blocks are less than 50 × 50 in dimension. Pedrouzo-Ulloa et al [13] proposed the solution for the privacy of PRNU-based camera attribution where the architecture are implemented using the homomorphically RLWE(Ring Learning with Errors)based cryptosystem combined with a pre-/post-coding which does not require access to a trusted environment.…”