Impact of photometric transformations on PRNU estimation schemes: A case study using near infrared ocular images

Abstract

The principle of Photo Response Non Uniformity (PRNU) is often used to link a digital image with the sensor that produced it. In this regard, a number of schemes have been proposed in the literature to extract PRNU details from a given input image. In this work, we study the impact of photometric transformations applied to near-infrared ocular images, on PRNU-based iris sensor identification accuracy. The contributions of this work are as follows: (a) Firstly, we evaluate the impact of 7 different photometric transformations on 4 different PRNU-based sensor identification schemes; (b) Secondly, we develop an explanatory model based on the Jensen-Shannon divergence measure to analyze the conditions under which these PRNU estimation schemes fail on photometrically transformed images. The analysis is conducted using 9,626 ocular images pertaining to 11 different iris sensors. Experiments suggest that (a) the Enhanced Sensor Pattern Noise and Maximum Likelihood Estimation based Sensor Pattern Noise techniques are more robust to photometric transformations than other PRNU-based schemes; (b) the application of photometric transformations actually improves the performance of the Phase Sensor Pattern Noise scheme; (c) the single-scale Self Quotient Image (SQI) and Difference of Gaussians (DoG) filtering transformations adversely impact all 4 PRNU-based schemes considered in this work; and (d) the Jensen-Shannon divergence measure is able to explain the degradation in performance of PRNU-based schemes as a function of the photometrically modified images.