# An Adaptive Method for Camera Attribution under Complex Radial Distortion Corrections

## TL;DR

This paper introduces an adaptive method that effectively handles complex radial distortion corrections in images, improving camera attribution accuracy and efficiency over existing approaches.

## Contribution

The proposed algorithm divides images into concentric annuli to better handle sophisticated radial distortions, and introduces the CPCE statistic for efficient early stopping.

## Key findings

- Improves camera attribution accuracy on radially distorted images.
- Reduces computational cost compared to previous methods.
- Effective on complex out-camera correction software like Adobe Lightroom.

## Abstract

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.