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arXiv 2104.09326cs.ITmath.IT
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基于学习辅助内容的随机分布时延感知系统的图像安全传输--扩展版

On Learning-Assisted Content-Based Secure Image Transmission for Delay-Aware Systems with Randomly-Distributed Eavesdroppers -- Extended Version

Mehdi Letafati, Hamid Behroozi, Babak Hossein Khalaj, Eduard A. Jorswieck

摘要:

在本文中,提出了一种学习辅助的基于内容的无线图像传输方案,其中多天线辅助源希望安全地提供一个图像到一个合法的目的地存在随机分布的窃听者(Eves)。我们考虑到这样一个事实,即从安全角度来看,并非图像的所有区域都具有相同的重要性。因此,我们提出了一种传输方案,其中源采用混合方法来实现包含不太重要的像素的公共区域的无错误数据传输;以及人工噪声(AN)辅助传输方案,为包含大量信息的区域提供安全性。此外,为了增强系统的安全性,采用了基于喷泉的数据包传输:首先,源节点将图像数据包编码成喷泉数据包,然后通过空中发送。如果合法的目的地正确地接收到整个图像源数据包,同时符合系统的延迟限制,我们提出的方案的保密性将实现之前,Eves可以获得的重要区域。因此,我们的计划的保密性能的特点是推导出封闭形式的安全质量(QoSec)违反概率的表达式。此外,我们提出的无线图像传输方案利用深度神经网络(DNN),并学习保持优化的传输参数,同时实现低QoSec违规概率。仿真结果提供了一些有用的工程见解,这表明我们提出的学习辅助方案优于国家的最先进的安全性和延迟要求方面取得了相当大的收益。

英文摘要:

In this paper, a learning-aided content-based wireless image transmission scheme is proposed, where a multi-antenna-aided source wishes to securely deliver an image to a legitimate destination in the presence of randomly distributed eavesdroppers (Eves). We take into account the fact that not all regions of an image have the same importance from the security perspective. Hence, we propose a transmission scheme, where the source employs a hybrid method to realize both the error-free data delivery of public regions containing less-important pixels; and an artificial noise (AN)-aided transmission scheme to provide security for the regions containing large amount of information. Moreover, in order to reinforce system's security, fountain-based packet delivery is adopted: First, the source node encodes image packets into fountain-like packets prior to sending them over the air. The secrecy of our proposed scheme will be achieved if the legitimate destination correctly receives the entire image source packets, while conforming to the latency limits of the system, before Eves can obtain the important regions. Accordingly, the secrecy performance of our scheme is characterized by deriving the closed-form expression for the quality-of-security (QoSec) violation probability. Moreover, our proposed wireless image delivery scheme leverages the deep neural network (DNN) and learns to maintain optimized transmission parameters, while achieving a low QoSec violation probability. Simulation results are provided with some useful engineering insights which illustrate that our proposed learning-assisted scheme outperforms the state-of-the-arts by achieving considerable gains in terms of security and the delay requirement.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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