On the SPN Estimation in Image Forensics: A Systematic Empirical Evaluation
On the SPN Estimation in Image Forensics: A Systematic Empirical Evaluation
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DOI：
10.1109/tifs.2016.2640938
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发表时间：
2017-05-01
影响因子：
6.8
通讯作者：
Khelifi, Fouad
中科院分区：
文献类型：
--
作者：
Al-Ani, Mustafa;Khelifi, Fouad
提取数码相机的指纹在图像取证中有着广泛的应用，如源相机识别和图像认证。在过去的十年中，光响应非均匀性（PRNU）已被公认为是数字成像器件可靠的唯一指纹。PRNU噪声在每幅图像中都是非常微弱的信号，其可靠的估计对于法医应用的成功率至关重要。在本文中，我们提出了一种新的方法评估21国家的最先进的PRNU估计/增强技术，已在文献中提出的各种框架。的技术进行分类和系统比较的基础上，他们的角色/阶段的PRNU估计程序，体现其内在的影响。每种技术的性能都在大规模实验中得到了广泛的证明，以结束这项区分大小写的研究。实验已经进行了我们创建的数据库和一个公共的图像数据库，“德累斯顿图像数据库。"
Extracting a fingerprint of a digital camera has fertile applications in image forensics, such as source camera identification and image authentication. In the last decade, photo response non_uniformity (PRNU) has been well established as a reliable unique fingerprint of digital imaging devices. The PRNU noise appears in every image as a very weak signal, and its reliable estimation is crucial for the success rate of the forensic application. In this paper, we present a novel methodical evaluation of 21 state-of-the-art PRNU estimation/enhancement techniques that have been proposed in the literature in various frameworks. The techniques are classified and systematically compared based on their role/stage in the PRNU estimation procedure, manifesting their intrinsic impacts. The performance of each technique is extensively demonstrated over a large-scale experiment to conclude this case-sensitive study. The experiments have been conducted on our created database and a public image database, the "Dresden image database."