LiDiNet: A Lightweight Deep Invertible Network for Image-in-Image Steganography
Fengyong Li, Sheng Yang, Kui Wu, Chuan Qin, Xinpeng Zhang
Abstract
This paper introduces a novel, lightweight deep invertible steganography network (LiDiNet) for image-in-image steganography. Traditional methods, while hiding a secret image within a cover image, often suffer from contour shadows or color distortion, making the secret image easily detectable. Additionally, the superposition of multiple invertible networks may complicate network structures and introduce excessive parameters, making the network training and learning processes difficult. LiDiNet addresses these issues by employing multiple invertible neural networks (INNs) to create a pair of coupled invertible processes for image hiding and recovery. A key innovation is the invertible convolutional layer, which streamlines the affine coupling structure in each INN for improved information fusion. In addition, a series of adaptive coordination spatial-wise attention modules are integrated to enhance the network’s effectiveness in image hiding and recovery, thereby elevating the security of the steganography. LiDiNet’s lightweight structure ensures both high-capacity steganography and robustness against steganalysis. Extensive experiments across various image datasets demonstrate LiDiNet’s superior performance, particularly in visual quality and anti-steganalysis capability, compared to existing methods.