Computed Tomography Image Origin Identification Based on Original Sensor Pattern Noise and 3-D Image Reconstruction Algorithm Footprints
Computed Tomography Image Origin Identification Based on Original Sensor Pattern Noise and 3-D Image Reconstruction Algorithm Footprints
复制标题
基于原始传感器图案噪声和 3D 图像重建算法足迹的计算机断层扫描图像原点识别
DOI：
10.1109/jbhi.2016.2575398
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发表时间：
2017-07-01
影响因子：
7.7
通讯作者：
Coatrieux, Gouenou
中科院分区：
文献类型：
--
作者：
Duan, Yuping;Bouslimi, Dalel;Coatrieux, Gouenou
在这篇文章中，我们关注的是已经产生CT图像的CT扫描仪的“盲”识别。为此，我们提出了一组来自图像链采集的噪声特征，这些特征可以作为CT扫描仪的足迹。基本上，我们提出了两种方法。第一个目标是基于X射线探测器固有的原始传感器模式噪声(OSPN)来识别CT扫描仪。第二个是基于噪声被其三维(3-D)图像重建算法修正的方式来识别采集系统。由于这些重建算法依赖于制造商并且是保密的，我们使用我们的特征作为输入来训练基于支持向量机的分类器来区分捕获系统。在4家不同厂家的15种不同型号的CT扫描仪上进行的实验表明，该系统对一幅CT图像的来源识别至少有94%的检测率，并且比一般公共摄像设备提出的基于传感器模式噪声(SPN)的策略具有更好的性能。
In this paper, we focus on the "blind" identification of the computed tomography (CT) scanner that has produced a CT image. To do so, we propose a set of noise features derived from the image chain acquisition and which can be used as CT-scanner footprint. Basically, we propose two approaches. The first one aims at identifying a CT scanner based on an original sensor pattern noise (OSPN) that is intrinsic to the X-ray detectors. The second one identifies an acquisition system based on the way this noise is modified by its three-dimensional (3-D) image reconstruction algorithm. As these reconstruction algorithms are manufacturer dependent and kept secret, our features are used as input to train a support vector machine (SVM) based classifier to discriminate acquisition systems. Experiments conducted on images issued from 15 different CT-scanner models of 4 distinct manufacturers demonstrate that our system identifies the origin of one CT image with a detection rate of at least 94% and that it achieves better performance than sensor pattern noise (SPN) based strategy proposed for general public camera devices.