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

arXiv: https://arxiv.org/abs/2302.14409 · HTML: https://mlsanity.com/paper/2302.14409/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [A leak in PRNU based source identification. Questioning fingerprint uniqueness](https://mlsanity.com/paper/2009.04878/) (2020)
- [Facing Device Attribution Problem for Stabilized Video Sequences](https://mlsanity.com/paper/1811.01820/) (2018)
- [Camera Fingerprint Extraction via Spatial Domain Averaged Frames](https://mlsanity.com/paper/1909.04573/) (2019)

## Built on by (most cited first, 1 of 1)

- [Diagonal Artifacts in Samsung Images: PRNU Challenges and Solutions](https://mlsanity.com/paper/2510.09509/) (2025)

## Abstract (arXiv)

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.