# Implicit Steganography Beyond the Constraints of Modality

arXiv: https://arxiv.org/abs/2312.05496 · HTML: https://mlsanity.com/paper/2312.05496/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 9 Dec 2023.

## Builds on (most cited first, 18 of 18)

- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Learned Initializations for Optimizing Coordinate-Based Neural Representations](https://mlsanity.com/paper/2012.02189/) (2020)
- [COIN: COmpression with Implicit Neural representations](https://mlsanity.com/paper/2103.03123/) (2021)
- [From data to functa: Your data point is a function and you can treat it like one](https://mlsanity.com/paper/2201.12204/) (2022)
- [Implicit Neural Representations for Image Compression](https://mlsanity.com/paper/2112.04267/) (2021)
- [CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography](https://mlsanity.com/paper/2305.16936/) (2023)
- [Meta-Learning Sparse Implicit Neural Representations](https://mlsanity.com/paper/2110.14678/) (2021)
- [StegaNeRF: Embedding Invisible Information within Neural Radiance Fields](https://mlsanity.com/paper/2212.01602/) (2022)
- [Large-capacity and Flexible Video Steganography via Invertible Neural Network](https://mlsanity.com/paper/2304.12300/) (2023)
- [SSGAN: Secure Steganography Based on Generative Adversarial Networks](https://mlsanity.com/paper/1707.01613/) (2017)
- [Deep Convolutional Neural Network to Detect J-UNIWARD](https://mlsanity.com/paper/1704.08378/) (2017)
- [Three-Dimensional Mesh Steganography and Steganalysis: A Review](https://mlsanity.com/paper/2104.10203/) (2021)
- [Spatial Image Steganography Based on Generative Adversarial Network](https://mlsanity.com/paper/1804.07939/) (2018)
- [Deep Neural Networks based Invisible Steganography for Audio-into-Image Algorithm](https://mlsanity.com/paper/2102.09173/) (2021)
- [Deep Cross-Modal Steganography Using Neural Representations](https://mlsanity.com/paper/2307.08671/) (2023)

## Built on by (most cited first, 1 of 1)

- [MDSGen: Fast and Efficient Masked Diffusion Temporal-Aware Transformers for Open-Domain Sound Generation](https://mlsanity.com/paper/2410.02130/) (2024)

## Abstract (arXiv)

Cross-modal steganography is committed to hiding secret information of one modality in another modality. Despite the advancement in the field of steganography by the introduction of deep learning, cross-modal steganography still remains to be a challenge to the field. The incompatibility between different modalities not only complicate the hiding process but also results in increased vulnerability to detection. To rectify these limitations, we present INRSteg, an innovative cross-modal steganography framework based on Implicit Neural Representations (INRs). We introduce a novel network allocating framework with a masked parameter update which facilitates hiding multiple data and enables cross modality across image, audio, video and 3D shape. Moreover, we eliminate the necessity of training a deep neural network and therefore substantially reduce the memory and computational cost and avoid domain adaptation issues. To the best of our knowledge, in the field of steganography, this is the first to introduce diverse modalities to both the secret and cover data. Detailed experiments in extreme modality settings demonstrate the flexibility, security, and robustness of INRSteg.