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摄像头识别与共同来源识别:不匹配的相关值。

Camera-identification and common-source identification: The correlation values of mismatches.

作者信息

Mieremet Arjan

机构信息

Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB, The Hague, Netherlands.

出版信息

Forensic Sci Int. 2019 Aug;301:46-54. doi: 10.1016/j.forsciint.2019.05.008. Epub 2019 May 10.

Abstract

A robust and well-known way to identify the source of an image is the use of Photo Response Non-Uniformity. The ability to be able to extract the PRNU-pattern from images is both used for camera-identification (linking one or more images to a camera) and common-source identification (linking images to images). In this paper we focus on the correlation values of mismatches, i.e. the correlation between images made with different cameras. Although the correlation values of mismatches are close to zero, they are never exactly zero. In this paper we show that it is possible with an extremely simple formula to a priori estimate the typical range for mismatch correlation values. This simple formula can be used as a decision rule in digital camera identification to either perform a complete investigation including reference recordings (which is time consuming) or not. In common-source identification this simple formula can be used to provide a well-educated guess for the threshold value to the cluster algorithm instead of just arbitrarily trying a range of threshold values.

摘要

一种可靠且广为人知的识别图像来源的方法是使用光电响应非均匀性。从图像中提取PRNU模式的能力既用于相机识别(将一张或多张图像与一台相机关联),也用于共同来源识别(将图像与图像关联)。在本文中,我们关注不匹配的相关值,即不同相机拍摄的图像之间的相关性。尽管不匹配的相关值接近零,但它们永远不会恰好为零。在本文中,我们表明可以用一个极其简单的公式先验估计不匹配相关值的典型范围。这个简单的公式可以用作数码相机识别中的决策规则,以决定是否进行包括参考记录在内的全面调查(这很耗时)。在共同来源识别中,这个简单的公式可以用于为聚类算法的阈值提供一个有根据的猜测,而不是随意尝试一系列阈值。

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