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

arXiv: https://arxiv.org/abs/2410.10117 · HTML: https://mlsanity.com/paper/2410.10117/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 46 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 22 of 22)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction](https://mlsanity.com/paper/2003.10983/) (2020)
- [StegaStamp: Invisible Hyperlinks in Physical Photographs](https://mlsanity.com/paper/1904.05343/) (2019)
- [NeRV: Neural Representations for Videos](https://mlsanity.com/paper/2110.13903/) (2021)
- [Learning Shape Templates with Structured Implicit Functions](https://mlsanity.com/paper/1904.06447/) (2019)
- [Implicit Neural Representation for Cooperative Low-light Image Enhancement](https://mlsanity.com/paper/2303.11722/) (2023)
- [SteganoGAN: High Capacity Image Steganography with GANs](https://mlsanity.com/paper/1901.03892/) (2019)
- [CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields](https://mlsanity.com/paper/2307.11526/) (2023)
- [StegaNeRF: Embedding Invisible Information within Neural Radiance Fields](https://mlsanity.com/paper/2212.01602/) (2022)
- [WateRF: Robust Watermarks in Radiance Fields for Protection of Copyrights](https://mlsanity.com/paper/2405.02066/) (2024)
- [StegExpose - A Tool for Detecting LSB Steganography](https://mlsanity.com/paper/1410.6656/) (2014)
- [Purified and Unified Steganographic Network](https://mlsanity.com/paper/2402.17210/) (2024)
- [Hiding Images in Deep Probabilistic Models](https://mlsanity.com/paper/2210.02257/) (2022)
- [Steganography of Steganographic Networks](https://mlsanity.com/paper/2302.14521/) (2023)
- [Hiding Functions within Functions: Steganography by Implicit Neural Representations](https://mlsanity.com/paper/2312.04743/) (2023)
- [Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise](https://mlsanity.com/paper/2401.01216/) (2024)
- [Multi-Image Steganography Using Deep Neural Networks](https://mlsanity.com/paper/2101.00350/) (2021)
- [Deep Cross-Modal Steganography Using Neural Representations](https://mlsanity.com/paper/2307.08671/) (2023)
- [Towards Deep Network Steganography: From Networks to Networks](https://mlsanity.com/paper/2307.03444/) (2023)

## Abstract (arXiv)

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.