With the rapid development of technology, smart environments utilizing the Internet of Things, artificial intelligence, and big data are improving the quality of life and work efficiency through connected devices. However, these advances present significant security challenges. The data generated by these smart devices contains many private and sensitive information. In data transmission, crime and terrorism may intercept this sensitive information and use it for secret communications and illegal activities. Steganography hides information in media files and prevents information leakage and interception by criminal and terrorist networks in an intelligent environment. It is an important technology to protect data integrity and security. Traditional steganography techniques often cause detectable distortions, whereas Steganography Without Embedding (SWE) avoids direct modification of cover media, thereby minimizing detection risks. This paper introduces an innovative and robust technique called Robust Linked List (RLL)-SWE, which improves resistance to attacks compared to traditional methods. Using multiple median downsampling and gradient calculations, this method extracts stable features. It restructures them into a multi-head unidirectional linked list, ensuring accurate message retrieval and high resistance to adversarial attacks. Comprehensive analysis and simulation experiments confirm the technique’s exceptional effectiveness and steganographic capacity.

中文翻译：

---

RLL-SWE： 智能环境中智能网络的鲁棒链表隐写术


随着技术的快速发展，利用物联网、人工智能和大数据的智能环境正在通过互联设备提高生活质量和工作效率。但是，这些进步带来了重大的安全挑战。这些智能设备生成的数据包含许多私人和敏感信息。在数据传输过程中，犯罪和恐怖主义可能会拦截这些敏感信息并将其用于秘密通信和非法活动。隐写术将信息隐藏在媒体文件中，防止信息泄露和被犯罪和恐怖分子网络在智能环境中拦截。它是保护数据完整性和安全性的重要技术。传统的隐写技术通常会导致可检测的失真，而无嵌入隐写术 （SWE） 避免了直接修改覆盖介质，从而最大限度地降低了检测风险。本文介绍了一种称为稳健链表 （RLL）-SWE 的创新且强大的技术，与传统方法相比，该技术提高了对攻击的抵抗力。该方法使用多个中位数下采样和梯度计算，提取稳定的特征。它将它们重组为多头单向链表，确保准确的消息检索和对对抗性攻击的高抵抗力。全面的分析和模拟实验证实了该技术的非凡有效性和隐写能力。