# StegNet: Mega Image Steganography Capacity with Deep Convolutional Network

arXiv: https://arxiv.org/abs/1806.06357 · HTML: https://mlsanity.com/paper/1806.06357/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2018.

## Built on by (most cited first, 14 of 14)

- [RoSteALS: Robust Steganography using Autoencoder Latent Space](https://mlsanity.com/paper/2304.03400/) (2023)
- [TrustMark: Universal Watermarking for Arbitrary Resolution Images](https://mlsanity.com/paper/2311.18297/) (2023)
- [Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography](https://mlsanity.com/paper/2107.09287/) (2021)
- [Learning to Immunize Images for Tamper Localization and Self-Recovery](https://mlsanity.com/paper/2210.15902/) (2022)
- [From Image to Imuge: Immunized Image Generation](https://mlsanity.com/paper/2110.14196/) (2021)
- [A Brief Survey on Deep Learning Based Data Hiding](https://mlsanity.com/paper/2103.01607/) (2021)
- [DeepFormableTag: End-to-end Generation and Recognition of Deformable Fiducial Markers](https://mlsanity.com/paper/2206.08026/) (2022)
- [Multitask Identity-Aware Image Steganography via Minimax Optimization](https://mlsanity.com/paper/2107.05819/) (2021)
- [Towards Robust Image-in-Audio Deep Steganography](https://mlsanity.com/paper/2303.05007/) (2023)
- [StegaPos: Preventing Unwanted Crops and Replacements with Imperceptible Positional Embeddings](https://mlsanity.com/paper/2104.12290/) (2021)
- [Deniable Steganography](https://mlsanity.com/paper/2205.12587/) (2022)
- [Exploring AI in Steganography and Steganalysis: Trends, Clusters, and Sustainable Development Potential](https://mlsanity.com/paper/2511.12052/) (2025)
- [Breaking the Resolution Barrier: Arbitrary-resolution Deep Image Steganography Framework](https://mlsanity.com/paper/2601.15739/) (2026)
- [StegGNN: Learning Graphical Representation for Image Steganography](https://mlsanity.com/paper/2609.32362/) (2026)

## Abstract (arXiv)

Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We~further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.