Sensor Pattern Noise Estimation Based on Improved Locally Adaptive DCT Filtering and Weighted Averaging for Source Camera Identification and Verification
Sensor Pattern Noise Estimation Based on Improved Locally Adaptive DCT Filtering and Weighted Averaging for Source Camera Identification and Verification
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DOI：
10.1109/tifs.2016.2620280
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发表时间：
2017-02
影响因子：
6.8
通讯作者：
Ashref Lawgaly;F. Khelifi
中科院分区：
文献类型：
--
作者：
Ashref Lawgaly;F. Khelifi
光响应非均匀性（PRNU）噪声是表征成像设备的传感器图案噪声。它已被广泛用于文献中的源相机识别和图像认证。传感器模式噪声在频率内容方面携带的丰富信息使其具有唯一性，因此适合于识别源相机和检测图像失真。然而，PRNU提取过程不可避免地面临着图像相关信息以及其他非唯一噪声分量的存在。为了减少这种不良影响，研究人员已经在该过程的不同阶段开发了许多技术，即，滤波阶段、估计阶段和后估计阶段。在本文中，我们提出了一个新的基于PRNU的源相机识别和验证系统，并提出了在不同阶段的增强。首先，在滤波阶段提出了一种改进的局部自适应离散余弦变换滤波器。在估计阶段，提出了一种新的加权平均技术。后估计阶段包括级联从颜色平面估计的PRNU，以便利用不同通道中物理PRNU分量的存在。两个图像数据集的实验结果获得了不同的相机设备已经示出了显着的增益，在每个阶段中所提出的增强，以及整体系统的优越性超过相关的国家的最先进的系统。
Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. It has been broadly used in the literature for source camera identification and image authentication. The abundant information that the sensor pattern noise carries in terms of the frequency content makes it unique, and hence suitable for identifying the source camera and detecting image forgeries. However, the PRNU extraction process is inevitably faced with the presence of image-dependent information as well as other non-unique noise components. To reduce such undesirable effects, researchers have developed a number of techniques in different stages of the process, i.e., the filtering stage, the estimation stage, and the post-estimation stage. In this paper, we present a new PRNU-based source camera identification and verification system and propose enhancements in different stages. First, an improved version of the locally adaptive discrete cosine transform filter is proposed in the filtering stage. In the estimation stage, a new weighted averaging technique is presented. The post-estimation stage consists of concatenating the PRNUs estimated from color planes in order to exploit the presence of physical PRNU components in different channels. Experimental results on two image data sets acquired by various camera devices have shown a significant gain obtained with the proposed enhancements in each stage as well as the superiority of the overall system over related state-of-the-art systems.