论文标题
深度联合源通道和加密编码：安全的语义通信
Deep Joint Source-Channel and Encryption Coding: Secure Semantic Communications
论文作者
论文摘要
深度学习驱动的联合源通道编码（JSCC）用于无线图像或视频传输（也称为DeepJSCC）最近一直是一个令人感兴趣的话题，结果非常有希望。这个想法是将类似的源样本映射到通道输入空间中的附近点，以便尽管通道引入了噪声，但可以以最小的失真恢复输入。在DeepJSCC中，这是通过自动编码器体系结构在编码器和解码器之间具有不可训练的通道层实现的。 DeepJSCC具有许多有利的属性，例如比单独的源和频道编码对应的端到端扭曲性能更好，并且在频道质量方面优美的退化。但是，由于源样本和通道输入之间的固有相关性，DEEPJSCC容易受到窃听攻击的影响。在本文中，我们提出了第一个用于无线图像传输的DEEPJSCC方案，该方案可针对窃听器（称为DeepJscec）安全。 DeepJScec不仅保留了DeepJSCC的有利属性，还提供了针对Eavesdropper所选的PlaintExt攻击的安全性，而无需对Evesdropper的通道状况或其预期使用截距信号的限制。数值结果表明，DEEPJSCEC的图像质量与使用BPG压缩，AES加密和LDPC代码用于通道编码的单独源编码相似或更好的图像质量，同时保留了图像质量在通道质量方面的优雅降级。我们还表明，提出的加密方法是问题不可知的，这意味着它可以应用于其他端到端的JSCC问题，例如远程分类，而无需修改。鉴于安全在现代无线通信系统中的重要性，我们认为这项工作使DeepJSCC计划在实践中更接近采用。
Deep learning driven joint source-channel coding (JSCC) for wireless image or video transmission, also called DeepJSCC, has been a topic of interest recently with very promising results. The idea is to map similar source samples to nearby points in the channel input space such that, despite the noise introduced by the channel, the input can be recovered with minimal distortion. In DeepJSCC, this is achieved by an autoencoder architecture with a non-trainable channel layer between the encoder and decoder. DeepJSCC has many favorable properties, such as better end-to-end distortion performance than its separate source and channel coding counterpart as well as graceful degradation with respect to channel quality. However, due to the inherent correlation between the source sample and channel input, DeepJSCC is vulnerable to eavesdropping attacks. In this paper, we propose the first DeepJSCC scheme for wireless image transmission that is secure against eavesdroppers, called DeepJSCEC. DeepJSCEC not only preserves the favorable properties of DeepJSCC, it also provides security against chosen-plaintext attacks from the eavesdropper, without the need to make assumptions about the eavesdropper's channel condition, or its intended use of the intercepted signal. Numerical results show that DeepJSCEC achieves similar or better image quality than separate source coding using BPG compression, AES encryption, and LDPC codes for channel coding, while preserving the graceful degradation of image quality with respect to channel quality. We also show that the proposed encryption method is problem agnostic, meaning it can be applied to other end-to-end JSCC problems, such as remote classification, without modification. Given the importance of security in modern wireless communication systems, we believe this work brings DeepJSCC schemes much closer to adoption in practice.
