A Universal Framework for Improving the Robustness of Coverless Image Steganography Based on Image Restoration

Abstract

Compared with traditional modification image steganography, coverless image steganography can resist the detection of steganalysis algorithms relying on no modification to the carriers. Previous works have made great efforts to improve the robustness against image attacks. However, the robustness of resisting geometric attacks performs not that well. After studying the general flow of the coverless image steganography, we find out that the receiver always needs to generate or map the hash sequences directly from the received images, which causes a significantly negative impact for extracting correct secret information because these received images might be attacked. Inspired by this finding, we surprisingly explore a common way to solve the problem by proposing a universal restoration framework for the attacked images. The most important module of the framework, the restoration module, contains two main parts, i.e., the classification sub-module and the attack restoration sub-module. The attacked images at the receiving end are first sent to a classification sub-module to estimate the type of the attack. Then, the corresponding attack restoration sub-module is utilized to repair the attacked images to improve the robustness. Experimental results show that the robustness of the existing coverless image steganography methods have been greatly improved after using the proposed framework without introducing extra security issues.