StegFlow: Flow-Based High-Frequency Distribution Mapping Network for Multi-Image Steganography

Multi-image steganography refers to the technique of embedding multiple secret images into a single cover image while ensuring that the secret images remain imperceptible and can be perfectly recovered by the recipient. Traditional single-image-based steganography often leads to noticeable contour shadows or color distortions in the cover image, making the hidden image more detectable. In contrast, cascaded invertible neural network-based steganography introduces a large number of parameters, complicating the network structure and resulting in a time-consuming learning and training process. To address the above problems, this paper proposes a novel flow-based, end-to-end multi-image invertible steganography framework (StegFlow), which effectively integrates forward and backward data flows for image hiding and recovery. The framework employs cascading operations to enable deep hiding of multiple secret images. To enhance the coupling capabilities, we introduce an invertible permutation layer that disrupts the channel arrangement order, allowing the coupling layer to more accurately guide the embedding of secret information into regions of the image that are easy to hide and recover. In addition, a high-frequency distribution mapping (HFDM) is designed to model the lost high-frequency information during image hiding process, significantly improving the recovery performance of the secret images. Extensive experiments are performed over multiple classical datasets, and the results demonstrate that compared to state-of-the-art (SOTA) models, the proposed framework can achieve a superior overall performance in terms of visual quality and anti-steganalysis capability. Specifically, our scheme can improve the hiding accuracy (measured by PSNR) by over 3 dB and the recovery accuracy by over 1 dB when hiding two secret images.