# Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography

arXiv: https://arxiv.org/abs/2107.09287 · HTML: https://mlsanity.com/paper/2107.09287/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 104 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [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)
- [Entangled Watermarks as a Defense against Model Extraction](https://mlsanity.com/paper/2002.12200/) (2020)
- [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)
- [Hidden Backdoors in Human-Centric Language Models](https://mlsanity.com/paper/2105.00164/) (2021)
- [SteganoGAN: High Capacity Image Steganography with GANs](https://mlsanity.com/paper/1901.03892/) (2019)
- [ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks](https://mlsanity.com/paper/1810.07248/) (2018)
- [A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions](https://mlsanity.com/paper/2011.06801/) (2020)
- [Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection](https://mlsanity.com/paper/1907.09177/) (2019)
- [DVMark: A Deep Multiscale Framework for Video Watermarking](https://mlsanity.com/paper/2104.12734/) (2021)
- [REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data](https://mlsanity.com/paper/1911.07205/) (2019)
- [StegExpose - A Tool for Detecting LSB Steganography](https://mlsanity.com/paper/1410.6656/) (2014)
- [Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings](https://mlsanity.com/paper/2104.13450/) (2021)
- [CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography](https://mlsanity.com/paper/1803.09043/) (2018)
- [Steganographic Generative Adversarial Networks](https://mlsanity.com/paper/1703.05502/) (2017)
- [Invisible Steganography via Generative Adversarial Networks](https://mlsanity.com/paper/1807.08571/) (2018)

## Built on by (most cited first, 24 of 24)

- [Watermarking Images in Self-Supervised Latent Spaces](https://mlsanity.com/paper/2112.09581/) (2021)
- [Visibility into AI Agents](https://mlsanity.com/paper/2401.13138/) (2024)
- [Authenticated Delegation and Authorized AI Agents](https://mlsanity.com/paper/2501.09674/) (2025)
- [Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms](https://mlsanity.com/paper/2212.12678/) (2022)
- [IDs for AI Systems](https://mlsanity.com/paper/2406.12137/) (2024)
- [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023)
- [Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges](https://mlsanity.com/paper/2405.17454/) (2024)
- [SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI](https://mlsanity.com/paper/2411.11478/) (2024)
- [The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields](https://mlsanity.com/paper/2502.19125/) (2025)
- [A Compact Neural Network-based Algorithm for Robust Image Watermarking](https://mlsanity.com/paper/2112.13491/) (2021)
- [Re-Key-Free, Risky-Free: Adaptable Model Usage Control](https://mlsanity.com/paper/2511.18772/) (2025)
- [PRIS: Practical robust invertible network for image steganography](https://mlsanity.com/paper/2309.13620/) (2023)
- [Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems](https://mlsanity.com/paper/2606.28425/) (2026)
- [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)
- [SIGMark: Scalable In-Generation Watermark with Blind Extraction for Video Diffusion](https://mlsanity.com/paper/2603.02882/) (2026)
- [Fragile Watermarking for Image Certification Using Deep Steganographic Embedding](https://mlsanity.com/paper/2504.13759/) (2025)
- [Picking watermarks from noise (PWFN): an improved robust watermarking model against intensive distortions](https://mlsanity.com/paper/2405.05170/) (2024)
- [Deep Data Hiding for ICAO-Compliant Face Images: A Survey](https://mlsanity.com/paper/2508.19324/) (2025)
- [Model X-Ray: Detection of Hidden Malware in AI Model Weights using Few Shot Learning](https://mlsanity.com/paper/2409.19310/) (2024)
- [Steganographic Embeddings as an Effective Data Augmentation](https://mlsanity.com/paper/2502.15245/) (2025)
- [Slice or the Whole Pie? Utility Control for AI Models](https://mlsanity.com/paper/2508.06551/) (2025)
- [Foveation Improves Payload Capacity in Steganography](https://mlsanity.com/paper/2510.13151/) (2025)
- [A Training-Free Proactive Defense Against Partial Speech Manipulation via Self-Embedding Steganography](https://mlsanity.com/paper/2608.25285/) (2026)

## Abstract (arXiv)

The advancement of secure communication and identity verification fields has significantly increased through the use of deep learning techniques for data hiding. By embedding information into a noise-tolerant signal such as audio, video, or images, digital watermarking and steganography techniques can be used to protect sensitive intellectual property and enable confidential communication, ensuring that the information embedded is only accessible to authorized parties. This survey provides an overview of recent developments in deep learning techniques deployed for data hiding, categorized systematically according to model architectures and noise injection methods. The objective functions, evaluation metrics, and datasets used for training these data hiding models are comprehensively summarised. Additionally, potential future research directions that unite digital watermarking and steganography on software engineering to enhance security and mitigate risks are suggested and deliberated. This contribution furthers the creation of a more trustworthy digital world and advances Responsible AI.