# A Brief Survey on Deep Learning Based Data Hiding

arXiv: https://arxiv.org/abs/2103.01607 · HTML: https://mlsanity.com/paper/2103.01607/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 81 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 24 of 24)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [StegaStamp: Invisible Hyperlinks in Physical Photographs](https://mlsanity.com/paper/1904.05343/) (2019)
- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)
- [Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding](https://mlsanity.com/paper/2009.03015/) (2020)
- [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)
- [StegExpose - A Tool for Detecting LSB Steganography](https://mlsanity.com/paper/1410.6656/) (2014)
- [Invisible Steganography via Generative Adversarial Networks](https://mlsanity.com/paper/1807.08571/) (2018)
- [ROMark: A Robust Watermarking System Using Adversarial Training](https://mlsanity.com/paper/1910.01221/) (2019)
- [Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective](https://mlsanity.com/paper/2102.06479/) (2021)
- [StegNet: Mega Image Steganography Capacity with Deep Convolutional Network](https://mlsanity.com/paper/1806.06357/) (2018)
- [WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images](https://mlsanity.com/paper/1803.04805/) (2018)
- [Robust Spatial-spread Deep Neural Image Watermarking](https://mlsanity.com/paper/2005.11735/) (2020)
- [Multi-Stage Residual Hiding for Image-into-Audio Steganography](https://mlsanity.com/paper/2101.01872/) (2021)
- [Generative Steganography by Sampling](https://mlsanity.com/paper/1804.10531/) (2018)
- [Hide and Speak: Towards Deep Neural Networks for Speech Steganography](https://mlsanity.com/paper/1902.03083/) (2019)
- [Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch](https://mlsanity.com/paper/1511.04855/) (2015)
- [Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach](https://mlsanity.com/paper/2101.00973/) (2020)
- [Steganography using a 3 player game](https://mlsanity.com/paper/1907.06956/) (2019)
- [Detecting Adversarial Examples - A Lesson from Multimedia Forensics](https://mlsanity.com/paper/1803.03613/) (2018)
- [DeepMorph: A System for Hiding Bitstrings in Morphable Vector Drawings](https://mlsanity.com/paper/2011.09783/) (2020)

## Built on by (most cited first, 9 of 9)

- [Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms](https://mlsanity.com/paper/2212.12678/) (2022)
- [Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography](https://mlsanity.com/paper/2107.09287/) (2021)
- [Watermark Faker: Towards Forgery of Digital Image Watermarking](https://mlsanity.com/paper/2103.12489/) (2021)
- [Hiding Images in Deep Probabilistic Models](https://mlsanity.com/paper/2210.02257/) (2022)
- [A Brief Yet In-Depth Survey of Deep Learning-Based Image Watermarking](https://mlsanity.com/paper/2308.04603/) (2023)
- [Finding needles in a haystack: A Black-Box Approach to Invisible Watermark Detection](https://mlsanity.com/paper/2403.15955/) (2024)
- [A Screen-Shooting Resilient Document Image Watermarking Scheme using Deep Neural Network](https://mlsanity.com/paper/2203.05198/) (2022)
- [Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines](https://mlsanity.com/paper/2302.04977/) (2023)
- [Picking watermarks from noise (PWFN): an improved robust watermarking model against intensive distortions](https://mlsanity.com/paper/2405.05170/) (2024)

## Abstract (arXiv)

Data hiding is the art of concealing messages with limited perceptual changes. Recently, deep learning has enriched it from various perspectives with significant progress. In this work, we conduct a brief yet comprehensive review of existing literature for deep learning based data hiding (deep hiding) by first classifying it according to three essential properties (i.e., capacity, security and robustness), and outline three commonly used architectures. Based on this, we summarize specific strategies for different applications of data hiding, including basic hiding, steganography, watermarking and light field messaging. Finally, further insight into deep hiding is provided by incorporating the perspective of adversarial attack.