Light scattering due to interaction with a material has long been known to create speckle patterns. We have demonstrated that even though speckle patterns from different objects are very similar, they contain minute dissimilarities that can be used to differentiate between the originating scatterers. We first approached this problem using a convolutional neural network—a deep learning algorithm—to show that indeed specific speckle patterns can be linked to the respective materials creating them. We then progressed to use recorded speckle patterns created from different materials in order to measure statistical parameters that possess a well-defined physical meaning. Using these parameters gave similar scatterer recognition abilities while gaining insight on the physical reasons for these material-dependent statistical deviations.

中文翻译：

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通过散斑图案分析识别散射体

早就知道由于与材料相互作用而产生的光散射会产生斑点图案。我们已经证明，即使来自不同对象的散斑图样非常相似，它们也包含微小的差异，这些差异可用于区分原始散射体。我们首先使用卷积神经网络（一种深度学习算法）解决了这个问题，以证明确实可以将特定的散斑图案链接到创建它们的各个材料。然后，我们开始使用由不同材料创建的记录的斑点图案，以测量具有明确定义的物理意义的统计参数。使用这些参数可提供类似的散射体识别能力，同时深入了解这些物质相关的统计偏差的物理原因。