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Computer Science > Computer Vision and Pattern Recognition

arXiv:2302.14409 (cs)
[Submitted on 28 Feb 2023]

Title:An Adaptive Method for Camera Attribution under Complex Radial Distortion Corrections

Authors:Andrea Montibeller, Fernando Pérez-González
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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.
Comments: This paper was submitted to IEEE Transactions on Information Forensics & Security the July 28, 2022
Subjects: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Image and Video Processing (eess.IV)
Cite as: arXiv:2302.14409 [cs.CV]
  (or arXiv:2302.14409v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2302.14409
Journal reference: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 19, 2024
Related DOI: https://doi.org/10.1109/TIFS.2023.3318933

Submission history

From: Andrea Montibeller [view email]
[v1] Tue, 28 Feb 2023 08:44:00 UTC (1,304 KB)
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