iSCMIS:Spatial-Channel Attention Based Deep Invertible Network for Multi-Image Steganography

Fengyong Li, Yang Sheng, Xinpeng Zhang, Chuan Qin

Abstract: Multi-image steganography refers to a stegano- graphic method where a user tries to hide multiple confidential images within a single cover image, and all confidential images can be correspondingly recovered perfectly by the recipient. Multi-image steganography essentially belongs to a high-capacity image steganographic scheme, but such high hiding capacity may easily cause severe contour shadows or color distortion of steganographic images, resulting in a significant reduction in anti-steganalysis capability. To address the above problem, this article designs a deep invertible neural network by introducing spatial-channel joint attention mechanism, in which the confidential image hiding and recovery can be regarded as a pair of coupled invertible processes. Specifically, a series of simple invertible networks having the same structure are firstly used to construct a cascaded deep invertible neural network framework, in which multiple confidential images can be sequentially embedded into a single cover image through a series of flexible cascaded iterative operations. Subsequently, spatial-channel joint attention module is designed to re-construct invertible network model, which can guide the embedding of secret information into more secure image regions. Accordingly, this joint attention mechanism can effectively address the problem of visual quality and security degradation of steganographic images due to high embedding capacity. Extensive experiments demonstrate that our scheme can obtain superior performance over different large-scale image sets, and outperforms state-of-the art methods with higher visual quality and stronger anti-steganalysis capability.