检查PRNU在现代设备上的可用性

C. Albisani, Massimo Iuliani, Alessandro Piva
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引用次数: 4

摘要

图像源识别任务主要通过利用传感器模式噪声的独特轨迹来解决,这确保在比较从不同设备提取的模式时,即使是同一品牌或型号,误报率也可以忽略不计。然而,大多数最新的智能手机都配备了专有的相机内处理,可能会在属于不同传感器的图像中暴露出意想不到的相关模式。在本文中,我们首先强调,当在默认和散景模式下获取图像时,属于同一品牌的智能手机上可能会发生错误的来源归属。虽然散景模式由于特定的相机后处理而被证明引入了相关模式,但我们也表明自然图像也暴露了这样的问题,即使有平面图像的参考。此外,不同的相机模型暴露了不同的相关模式,因为它们与开发人员的选择有合理的关系。然后,我们提出了一种通用策略,允许法医从业者确定被质疑的设备是否可能遭受这些相关模式的影响,从而避免了错误图像归因的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Checking PRNU Usability on Modern Devices
The image source identification task is mainly addressed by exploiting the unique traces of the sensor pattern noise, that ensure a negligible false alarm rate when comparing patterns extracted from different devices, even of the same brand or model. However, most recent smartphones are equipped with proprietary in-camera processing that can possibly expose unexpected correlated patterns within images belonging to different sensors.In this paper, we first highlight that wrong source attribution can happen on smartphones belonging to the same brand when images are acquired both in default and in bokeh mode. While the bokeh mode is proved to introduce a correlated pattern due to the specific in-camera post-processing, we also show that natural images also expose such issue, even when a reference from flat images is available. Furthermore, different camera models expose different correlation patterns since they are reasonably related to developers’ choices. Then, we propose a general strategy that allows the forensic practitioner to determine whether a questioned device may suffer from these correlated patterns, thus avoiding the risk of false image attribution.
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