PRNU Emphasis: a Generalization of the Multiplicative Model

TL;DR

This paper enhances the PRNU model by accounting for nonlinear camera response functions, leading to improved device identification accuracy in multimedia forensics.

Contribution

It introduces a generalized PRNU model that considers nonlinear mappings beyond gamma correction and proposes a method to estimate this effect.

Findings

Deviations from gamma correction in camera responses.

Improved device identification accuracy by 4.93% on average.

Enhanced PRNU-based forensic analysis with the new model.

Abstract

The photoresponse non-uniformity (PRNU) is a camera-specific pattern, widely adopted to solve multimedia forensics problems such as device identification or forgery detection. The theoretical analysis of this fingerprint customarily relies on a multiplicative model for the denoising residuals. This setup assumes that the nonlinear mapping from the scene irradiance to the preprocessed luminance, that is, the composition of the Camera Response Function (CRF) with the optical and digital preprocessing pipelines, is a gamma correction. Yet, this assumption seldom holds in practice. In this letter, we improve the multiplicative model by including the influence of this nonlinear mapping on the denoising residuals. We also propose a method to estimate this effect. Results evidence that the response of typical cameras deviates from a gamma correction. Experimental device identification with our model increases the TPR by a $4.93\, \%$ on average for a fixed FPR of $0.01$.