Holograms are security features applied to security documents like banknotes, passports, and ID cards in order to protect them from counterfeiting. Checking the authenticity of holograms is an important but difficult task, as holograms comprise different appearances for varying observation and/or illumination directions. Multi-view and photometric image acquisition and analysis procedures have been proposed to capture that variable appearance. We have developed a portable ring-light illumination module used to acquire photometric image stacks of holograms with mobile devices. By the application of Convolutional Neural Networks (CNN), we developed a vector representation that captures the essential appearance properties of hologram types in only a few values extracted from the photometric hologram stack. We present results based on Euro banknote holograms of genuine and counterfeited Euro banknotes. When compared to a model-based hologram descriptor, we show that our new learned CNN representation enables hologram authentication on the basis of our mobile acquisition method more reliably.

中文翻译：

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深度学习的移动全息图验证

全息图是应用于防伪文件（如钞票，护照和身份证）的防伪特征，以防止伪造。检查全息图的真实性是重要但困难的任务，因为全息图包括用于不同观察和/或照明方向的不同外观。已经提出了多视图和光度图像获取和分析程序来捕获该可变外观。我们开发了一种便携式环形照明模块，用于通过移动设备获取全息图的光度图像堆栈。通过卷积神经网络（CNN）的应用，我们开发了一种矢量表示形式，该矢量表示形式仅从从光度全息图堆栈中提取的几个值中捕获了全息图类型的基本外观属性。我们根据真实和伪造的欧元纸币的欧元纸币全息图展示结果。当与基于模型的全息图描述符进行比较时，我们表明，我们新学习的CNN表示能够更可靠地基于我们的移动获取方法实现全息图身份验证。