Authenticating Video Feeds using Electric Network Frequency Estimation at the Edge
Authenticating Video Feeds using Electric Network Frequency Estimation at the Edge
复制标题
在边缘使用电网频率估计来验证视频源
DOI：
10.4108/eai.4-2-2021.168648
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
E. Blasch
中科院分区：
文献类型：
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作者：
Deeraj Nagothu;Yu Chen;Alexander J. Aved;E. Blasch
大规模视频物联网（IoVT）支持智能城市的态势感知;然而，阿尔蒂智能（AI）技术的快速发展使得虚假视频/音频流和篡改图像能够欺骗智能城市安全运营商。验证视频/音频馈送对于安全性和安全性变得至关重要，其中从电网收集的电网频率（ENF）信号是一种突出的验证机制。提出了一种基于ENF的稳定超像素视频认证方法（EVAS）。视频超像素将具有均匀强度和纹理的像素分组，以消除ENF估计中的波动的影响。大量的实验研究验证了EVAS系统的有效性。针对电网供电的边缘具有互连监控摄像机系统的环境，所提出的EVAS系统实现了检测图像序列中的不相似性的设计目标。
Large scale Internet of Video Things (IoVT) supports situation awareness for smart cities; however, the rapid development in artificial intelligence (AI) technologies enables fake video/audio streams and doctored images to fool smart city security operators. Authenticating visual/audio feeds becomes essential for safety and security, from which an Electric Network Frequency (ENF) signal collected from the power grid is a prominent authentication mechanism. This paper proposes an ENF-based Video Authentication method using steady Superpixels (EVAS). Video superpixels group the pixels with uniform intensities and textures to eliminate the impacts from the fluctuations in the ENF estimation. An extensive experimental study validated the e ff ectiveness of the EVAS system. Aiming at the environments with interconnected surveillance camera systems at the edge powered by an electricity grid, the proposed EVAS system achieved the design goal of detecting dissimilarities in the image sequences.