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.

翻译：相机内或相机外软件/固件施加的径向校正畸变会改变图像的支撑网格，从而干扰基于PRNU的相机源辨识。现有解决方案试图通过参数化少数变量的径向变换来逆向/估计校正，以限制计算负荷；然而，面对日益普遍的复杂畸变校正，其性能难以令人满意。本文提出一种自适应算法，通过将图像划分为同心圆环，能够处理诸如Adobe Lightroom、Photoshop、Gimp及PT-Lens等第三方软件在相机外施加的复杂校正。我们还引入了一种名为累积相关能量峰值（CPCE）的统计量，可实现高效的早停策略。在包含相机内和相机外径向校正图像的大型数据集上的实验表明，我们的解决方案在准确率和计算成本两方面均提升了现有技术水平。