Mutual Information Guided Invertible Image Hiding Network

Abstract

Image hiding techniques are commonly used for secure communication, copyright protection, and visual privacy. Invertible neural network (INN) have emerged as a promising approach for image steganography, enabling the concealment and recovery of secret images through forward and backward mappings within the network. However, existing methods often face limitations in the accuracy of recovered images due to challenges in estimating the lost information during the forward process. To address this issue, we propose a Mutual Information Guided Invertible Image Hiding Network (MIGIIHNet), which leverages mutual information estimation between the lost information and the stego image in the forward process to guide the backward mapping for reconstruction. Specifically, we propose a lightweight INN with a channel attention feature aggregation module (CAFAM), integrating a channel attention mechanism to optimize the multi-scale aggregation of both low-level and high-level features in a single forward pass. Also, an association learning module (ALM) is designed to model the mutual information between the stego image and the lost information during the forward hiding process. Then, the mutual information is utilized to reconstruct the secret image with high accuracy. Extensive experimental results show that MIGIIHNet outperforms existing state-of-the-art methods in terms of invisibility, security, and recovery accuracy, while maintaining low computational complexity.