Robust Coverless Image Steganography Based on Neglected Coverless Image Dataset Construction

Abstract

Most of the existing image selection-based coverless image steganography methods mainly focus on improving the capacity and robustness under the assumption that the corresponding dataset is available. But they ignore how to successfully construct the coverless image dataset, which is the foundation of such methods and has a critical impact on the capacity. In this paper, a coverless image steganography is proposed that considers how to efficiently construct the coverless image dataset. In the proposed method, the CNN-based deep hash is extracted from the image and a specific mapping rule is designed to map the high-dimensional deep hash to the low-dimensional secret message. In addition, an unsupervised clustering algorithm is adopted to construct the coverless image dataset, which makes the construction of the coverless image dataset efficient and improves the robustness of the proposed steganography method. To our best knowledge, this is the first attempt to improve the construction efficiency of the coverless image dataset in the field of coverless image steganography. Experimental results show that the construction of a large coverless image dataset is feasible and reliable, and the proposed method has better robustness and higher dataset utilization rate compared with the state-of-the-art methods.