NiNet: A new invertible neural network architecture more suitable for deep image hiding

Abstract

Image hiding through the application of invertible neural network (INN) represents a significant branch within the realm of deep image hiding methodologies, characterized by a compact network architecture and a streamlined parameter count. Nonetheless, when juxtaposed with autoencoder-based approaches, existing INN methods often result in inferior image quality. To surmount this challenge, this paper introduces a novel masking-based image hiding paradigm, establishes a new spatial domain transformation for images, and refines the Swin-transformer block. By integrating these innovations, an INN architecture is crafted that is particularly adept for deep image hiding, termed NiNet. The experimental results demonstrate that NiNet can remarkably address the problem of image hiding. In terms of steganographic image quality, NiNet outperforms the current state-of-the-art (SOTA) algorithms by 0.26 dB on the DIV2K dataset, 1.49 dB on the COCO dataset, and 0.39 dB on the ImageNet dataset. Regarding the quality of secret image recovery, NiNet surpasses the SOTA algorithms by 2.06 dB on DIV2K, 1.98 dB on COCO, and 0.50 dB on ImageNet.