Modern infrastructure systems, such as bridges, dams, power generation stations, and buildings increasingly have an intrinsic cyber-physical nature to them. Infrastructure now commonly, includes actuators, network connections, sensors, control systems, and computational resources. It is of increasing concern that modern infrastructure is vulnerable to cyber-attacks that can damage both the cyber and physical nature of the infrastructure. To date, the physical and cyber health of infrastructure has been considered separately. However, the increasing concerns associated with the cyber-physical security of infrastructure coupled with the emergence of 5G networks made using components that are not universally considered trustworthy, and the emergence of techniques for creating deepfakes and adversarial examples suggests the time has come to begin considering cyber health and structural health with a more holistic approach. In this work, a protocol is developed for ensuring the imagery data captured by a structural health monitoring system can be unambiguously attributed to legitimate sensors associated with the structural health monitoring system. A computer vision approach based on the idea of mutual information is then presented to detect damage in an image. This work presents the protocol for authenticating the provenance of imager data and demonstrates that this protocol does not have overly adverse effects when used with the mutual information-based technique for detecting damage in the resulting imagery data.

诸如桥梁，水坝，发电站和建筑物之类的现代基础设施系统越来越具有内在的网络物理性质。现在，基础设施通常包括执行器，网络连接，传感器，控制系统和计算资源。越来越令人担忧的是，现代基础架构容易受到网络攻击，从而可能破坏基础架构的网络和物理性质。迄今为止，基础设施的物理和网络健康状况已被分别考虑。但是，与基础架构的网络物理安全性相关联的关注日益增加，并且出现了5G网络的出现，这些网络是使用普遍认为不可信的组件制作的，并且，用于创建深造假和对抗性示例的技术的出现表明，是时候开始以更全面的方式考虑网络健康和结构健康了。在这项工作中，开发了一种协议，以确保可以将由结构健康监控系统捕获的图像数据明确地归因于与结构健康监控系统关联的合法传感器。然后提出了一种基于互信息的计算机视觉方法，以检测图像中的损坏。这项工作提出了用于验证成像器数据来源的协议，并证明了该协议与基于互信息的技术一起用于检测所得成像数据中的损坏时，不会产生过分不利的影响。