StegaEdge: learning edge-guidance steganography

Abstract

Steganography is critical in traceability, authentication, and secret delivery for multimedia. In this paper, we propose a novel image steganography framework, named StegaEdge, via learning edge-guidance network to simultaneously address three challenges, capacity, multi-task, and invisibility. First, we use an upsampling strategy to expand the embedding space and thus increase the capacity of the embedded message. Second, our algorithm improves the embedding way of messages so that it can handle different messages embedded in the same image and achieve split-task recovery completely. Different information can be embedded in one cover image without affecting each other. Third, we innovatively propose an edge-guidance strategy to solve the problem of poor invisibility in smooth regions. The human eye is significantly less perceptive of intensity changes in edges than in smooth areas. Unlike traditional steganography methods, our edge-guidance steganography can appropriately embed part of the information into non-edge regions when the amount of embedded information is too large. Experimental results on three datasets show that the newly proposed StegaEdge algorithm achieves satisfactory results in terms of capacity, multi-task, imperceptibility, and security compared to the state-of-the-art algorithms.