StegaINR4MIH: steganography by implicit neural representation for multi-image hiding

TL;DR

This paper introduces StegaINR4MIH, a novel neural representation-based steganography method for hiding multiple images within a single implicit function, achieving high quality and undetectability.

Contribution

It proposes a new implicit neural representation framework for multi-image hiding that uses parameter redundancy and secret weight substitution, differing from traditional encoder-based methods.

Findings

PSNR exceeds 42 for two secret images

PSNR exceeds 39 for five secret images

Superior visual quality and undetectability

Abstract

Multi-image hiding, which embeds multiple secret images into a cover image and is able to recover these images with high quality, has gradually become a research hotspot in the field of image steganography. However, due to the need to embed a large amount of data in a limited cover image space, issues such as contour shadowing or color distortion often arise, posing significant challenges for multi-image hiding. In this paper, we propose StegaINR4MIH, a novel implicit neural representation steganography framework that enables the hiding of multiple images within a single implicit representation function. In contrast to traditional methods that use multiple encoders to achieve multi-image embedding, our approach leverages the redundancy of implicit representation function parameters and employs magnitude-based weight selection and secret weight substitution on pre-trained cover image functions to effectively hide and independently extract multiple secret images. We conduct experiments on images with a resolution of from three different datasets: CelebA-HQ, COCO, and DIV2K. When hiding two secret images, the PSNR values of both the secret images and the stego images exceed 42. When hiding five secret images, the PSNR values of both the secret images and the stego images exceed 39. Extensive experiments demonstrate the superior performance of the proposed method in terms of visual quality and undetectability.