# PRIS: Practical robust invertible network for image steganography

arXiv: https://arxiv.org/abs/2309.13620 · HTML: https://mlsanity.com/paper/2309.13620/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 64 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Invertible Residual Networks](https://mlsanity.com/paper/1811.00995/) (2018)
- [Residual Flows for Invertible Generative Modeling](https://mlsanity.com/paper/1906.02735/) (2019)
- [Analyzing Inverse Problems with Invertible Neural Networks](https://mlsanity.com/paper/1808.04730/) (2018)
- [i-RevNet: Deep Invertible Networks](https://mlsanity.com/paper/1802.07088/) (2018)
- [Distortion Agnostic Deep Watermarking](https://mlsanity.com/paper/2001.04580/) (2020)
- [SteganoGAN: High Capacity Image Steganography with GANs](https://mlsanity.com/paper/1901.03892/) (2019)
- [Generating Steganographic Images via Adversarial Training](https://mlsanity.com/paper/1703.00371/) (2017)
- [ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks](https://mlsanity.com/paper/1810.07248/) (2018)
- [MintNet: Building Invertible Neural Networks with Masked Convolutions](https://mlsanity.com/paper/1907.07945/) (2019)
- [Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography](https://mlsanity.com/paper/2107.09287/) (2021)
- [SSGAN: Secure Steganography Based on Generative Adversarial Networks](https://mlsanity.com/paper/1707.01613/) (2017)
- [Unsupervised Steganalysis Based on Artificial Training Sets](https://mlsanity.com/paper/1703.00796/) (2017)
- [Generative Reversible Data Hiding by Image to Image Translation via GANs](https://mlsanity.com/paper/1905.02872/) (2019)
- [Spatial Image Steganography Based on Generative Adversarial Network](https://mlsanity.com/paper/1804.07939/) (2018)
- [Steganography using a 3 player game](https://mlsanity.com/paper/1907.06956/) (2019)

## Built on by (most cited first, 5 of 5)

- [Hiding Images in Diffusion Models by Editing Learned Score Functions](https://mlsanity.com/paper/2503.18459/) (2025)
- [Sanitizing Hidden Information with Diffusion Models](https://mlsanity.com/paper/2310.06951/) (2023)
- [STCL:Curriculum learning Strategies for deep learning image steganography models](https://mlsanity.com/paper/2504.17609/) (2025)
- [Stego Battlefield: Evaluating Image Steganography Attacks and Steganalysis Defenses](https://mlsanity.com/paper/2605.05789/) (2026)
- [DKiS: Decay weight invertible image steganography with private key](https://mlsanity.com/paper/2311.18243/) (2023)

## Abstract (arXiv)

Image steganography is a technique of hiding secret information inside another image, so that the secret is not visible to human eyes and can be recovered when needed. Most of the existing image steganography methods have low hiding robustness when the container images affected by distortion. Such as Gaussian noise and lossy compression. This paper proposed PRIS to improve the robustness of image steganography, it based on invertible neural networks, and put two enhance modules before and after the extraction process with a 3-step training strategy. Moreover, rounding error is considered which is always ignored by existing methods, but actually it is unavoidable in practical. A gradient approximation function (GAF) is also proposed to overcome the undifferentiable issue of rounding distortion. Experimental results show that our PRIS outperforms the state-of-the-art robust image steganography method in both robustness and practicability. Codes are available at https://github.com/yanghangAI/PRIS, demonstration of our model in practical at http://yanghang.site/hide/.