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ATM Protection Using Embedded Deep Learning Solutions

机译：ATM保护使用嵌入式深度学习解决方案

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Last decade advances in Deep Learning methods lead to sensible improvements in state of the art results in many real world applications, thanks to the exploitation of particular Artificial Neural Networks architectures. In this paper we present an investigation of the application of such kind of structures to a Video Surveillance case of study, in which the special nature and the small amount of available data increases the difficulties during the training phase. The analyzed scenario involves the protection of Automatic Teller Machines (ATM), representing a sensitive problem in. the world of both banking and public security. Because of the critical issues related to this environment, even apparently small improvements in either accuracy or responsiveness of surveillance systems can produce a fundamental contribution. Even if the experimentation has been reproduced in an artificial scenario, the results show that the implemented architecture is able to classify depth data in real-time on an embedded system, detecting all the test attacks in a few seconds.
机译：由于对特定人工神经网络架构的开发，最近十年的深度学习方法导致了最多的世界应用中的最具现实应用的明智改善。在本文中，我们展示了对研究的视频监控案例的应用对这种结构的应用，其中特殊性和少量可用数据增加了训练阶段的困难。分析的方案涉及保护自动柜员机（ATM），代表敏感问题。银行和公共安全的世界。由于与这种环境相关的关键问题，甚至显然的监测系统的准确性或响应性的少量改善也会产生基本贡献。即使实验已经在人工方案中被复制，结果表明，实现的架构能够在嵌入式系统上实时对深度数据进行分类，在几秒钟内检测所有测试攻击。

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