# A Novel Approach to Image Steganography Using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2412.00094 · HTML: https://mlsanity.com/paper/2412.00094/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [SteganoGAN: High Capacity Image Steganography with GANs](https://mlsanity.com/paper/1901.03892/) (2019)
- [ColorNet: Investigating the importance of color spaces for image classification](https://mlsanity.com/paper/1902.00267/) (2019)
- [Steganographic Generative Adversarial Networks](https://mlsanity.com/paper/1703.05502/) (2017)
- [StegColNet: Steganalysis based on an ensemble colorspace approach](https://mlsanity.com/paper/2002.02413/) (2020)

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

- [Quantum Gatekeeper: Multi-Factor Context-Bound Image Steganography with VQC Based Key Derivation on Quantum Hardware](https://mlsanity.com/paper/2604.26413/) (2026)

## Abstract (arXiv)

The field of steganography has long been focused on developing methods to securely embed information within various digital media while ensuring imperceptibility and robustness. However, the growing sophistication of detection tools and the demand for increased data hiding capacity have revealed limitations in traditional techniques. In this paper, we propose a novel approach to image steganography that leverages the power of generative adversarial networks (GANs) to address these challenges. By employing a carefully designed GAN architecture, our method ensures the creation of stego-images that are visually indistinguishable from their original counterparts, effectively thwarting detection by advanced steganalysis tools. Additionally, the adversarial training paradigm optimizes the balance between embedding capacity, imperceptibility, and robustness, enabling more efficient and secure data hiding. We evaluate our proposed method through a series of experiments on benchmark datasets and compare its performance against baseline techniques, including least significant bit (LSB) substitution and discrete cosine transform (DCT)-based methods. Our results demonstrate significant improvements in metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and robustness against detection. This work not only contributes to the advancement of image steganography but also provides a foundation for exploring GAN-based approaches for secure digital communication.