Coverless Information Hiding Based on Probability Graph Learning for Secure Communication in IoT Environment
Zhili Zhou, Yuecheng Su, Yulan Zhang, Zhihua Xia, Shan Du, Brij B. Gupta, Lianyong Qi
Abstract
To securely transmit secret data between Internet of Things (IoT) nodes, it is required to the implement information hiding technique for secure communication in the IoT environment. The traditional information hiding approaches generally select a multimedia file, such as texts, images, and video clips as the cover, and then embed secret information into the cover by slight modification. However, it is not feasible to directly apply these approaches in the IoT environment for the following reasons. First, it is hard for some IoT nodes to effectively and efficiently process and transmit the complex multimedia data. Second, the modification trace left in the cover will cause the presence of hidden secret information to be easily exposed by steganalysis tools. To address the above issues, we propose a coverless information hiding scheme based on probability graph learning for secure communication in the IoT environment. Instead of modifying an existing multimedia cover, we conceal secret information in a generated sequence of IoT data to realize secure communication between different nodes in the IoT environment. According to the node-data interaction relationships, we first learn the transition probability graph (TPG) to describe the transition probabilities between IoT data elements. Then, guided by a given secret message that needs to be hidden, we sequentially select a set of highly correlated data elements from the TPG to generate the sequence. The experimental results and theoretical analysis demonstrate that the proposed information hiding scheme can achieve high hiding capacity with desirable imperceptibility and security performances in the IoT environment.