Sensor pattern noise has been found to be a reliable tool for providing information relating to the provenance of an image. Conventionally sensor pattern noise is modelled as a mutual interaction of pixel non-uniformity noise and dark current. By using a wavelet denoising filter it is possible to isolate a unique signal within a sensor caused by the way the silicon reacts non-uniformly to light. This signal is often referred to as a fingerprint. To obtain the estimate of this photo response non-uniformity multiple sample images are averaged and filtered to derive a noise residue. This process and model, while useful at providing insight into an images provenance, fails to take into account additional sources of noise that are obtained during this process. These other sources of noise include digital processing artefacts collectively known as camera noise, image compression artefacts, lens artefacts, and image content. By analysing the diversity of sources of noise remaining within the noise residue, we show that further insight is possible within a unified sensor pattern noise concept which opens the field to approaches for obtaining fingerprints utilising fewer resources with comparable performance to existing methods.
Device-based Image Matching with Similarity Learning by Convolutional Neural Networks that Exploit the Underlying Camera Sensor Pattern Noise
Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen（格罗宁根大学伯努利数学、计算机科学与人工智能研究所）；Universidad de León（莱昂大学）
PRNU Based Source Camera Identification for Webcam Videos
这篇文章是关于图像取证的应用，我们使用摄像头传感器指纹来识别网络摄像头视频中的源摄像头（SCI：Source Camera Identification）。传感器或相机指纹是基于计算由于制造缺陷而始终存在于此类传感器中的固有噪声。这是将每个传感器与其噪声模式联系起来的不可避免的特性。PRNU（照片响应非均匀性）已成为计算相机指纹的默认技术。现在有许多应用程序处理PRNU模式的相机识别使用静止图像。在这项工作中，我们专注于视频，更具体地说，网络摄像头视频，因为网络摄像头视频的重要性，现在。在这项工作中，三种可能的方法SCI的实施和评估。
Testing Robustness of Camera Fingerprint (PRNU) Detectors
在法医成像领域，重要的是能够从已知由同一相机拍摄的一个或一小组图像中提取“相机指纹”。理想情况下，该指纹将用于识别单个源相机。相机指纹是基于所有图像传感器中存在的某种随机噪声，这是由于制造缺陷，因此是独特的，无法避免的。PRNU（Photo-Response Non-Uniformity）已成为SCI（Source Camera Identification）中应用最广泛的方法。在本文中，我们设计了一套“攻击”的PRNU基于SCI系统，我们衡量每种方法的成功。我们将攻击方法理解为任何最低限度地改变图像质量的处理，并且旨在欺骗PRNU检测器（或者概括地说，任何相机指纹检测器）。基于PRNU的SCI系统取自一个公开可用的优秀参考文献。
