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摘要翻译：
传感器模式噪声已经被发现是提供与图像来源有关的信息的可靠工具。传统的传感器模式噪声被描述为像素非均匀性噪声和暗电流的相互作用。通过使用小波去噪滤波器，可以分离出传感器内由硅对光的非均匀反应引起的唯一信号。这种信号通常被称为指纹。为了获得这种光响应不均匀性的估计，对多个样本图像进行平均和滤波，以导出噪声残差。这个过程和模型虽然在提供对图像来源的洞察力方面很有用，但没有考虑到在这个过程中获得的额外噪声源。这些其他噪声源包括数字处理伪影，统称为相机噪声，图像压缩伪影，镜头伪影和图像内容。通过分析残留噪声中噪声源的多样性，我们表明，在统一的传感器模式噪声概念中，进一步的洞察力是可能的，该概念为利用更少的资源获得指纹的方法开辟了领域，并与现有方法具有相当的性能。
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英文标题：
《Rethinking Image Sensor Noise for Forensic Advantage》
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作者：
Richard Matthews, Matthew Sorell, Nickolas Falkner
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最新提交年份：
2019
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Physics 物理学
二级分类：Instrumentation and Methods for Astrophysics 天体物理学仪器和方法
分类描述：Detector and telescope design, experiment proposals. Laboratory Astrophysics. Methods for data analysis, statistical methods. Software, database design
探测器和望远镜设计，实验建议。实验室天体物理学。资料分析方法，统计方法。软件，数据库设计
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/1808.07971
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