An Adaptive Method for Camera Attribution under Complex Radial Distortion Corrections
This addresses the challenge of identifying cameras from images after complex distortion corrections, which is incremental for forensic analysis and security applications.
The paper tackles the problem of camera attribution under complex radial distortion corrections, which hampers PRNU-based methods, by proposing an adaptive algorithm that divides images into concentric annuli and introduces a CPCE statistic for early stopping. Experiments on a large dataset show improvements in both accuracy and computational cost compared to the state of the art.
Radial correction distortion, applied by in-camera or out-camera software/firmware alters the supporting grid of the image so as to hamper PRNU-based camera attribution. Existing solutions to deal with this problem try to invert/estimate the correction using radial transformations parameterized with few variables in order to restrain the computational load; however, with ever more prevalent complex distortion corrections their performance is unsatisfactory. In this paper we propose an adaptive algorithm that by dividing the image into concentric annuli is able to deal with sophisticated corrections like those applied out-camera by third party software like Adobe Lightroom, Photoshop, Gimp and PT-Lens. We also introduce a statistic called cumulative peak of correlation energy (CPCE) that allows for an efficient early stopping strategy. Experiments on a large dataset of in-camera and out-camera radially corrected images show that our solution improves the state of the art in terms of both accuracy and computational cost.