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Checking PRNU Usability on Modern Devices

机译:检查现代设备上的PRNU可用性

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摘要

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.
机译:图像源识别任务主要通过利用传感器图案噪声的独特迹线来解决,以确保在比较从不同设备中提取的模式,即使是相同的品牌或模型的模式时可以忽略不计的误报率。但是,最近的智能手机都配备了专有的相机处理,可以在属于不同传感器的图像内暴露出意外的相关模式。在本文中,我们首先突出显示错误的源归因可能发生在智能手机上,当图像是相同品牌的智能手机在默认和散景模式下获得。虽然散景模式被证明由于特定的相机后处理而引入相关模式,但我们还表明自然图像也暴露出这样的问题,即使从平面图像的参考提供。此外,不同的相机模型暴露不同的相关模式,因为它们与开发人员的选择合理相关。然后,我们提出了一种允许取证从业者确定质疑设备是否可能遭受这些相关模式的一般策略,从而避免了假图像归因的风险。

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