Recent Advances of Image Steganography with Generative Adversarial Networks
arXiv:1907.01886
Abstract
In the past few years, the Generative Adversarial Network (GAN) which proposed in 2014 has achieved great success. GAN has achieved many research results in the field of computer vision and natural language processing. Image steganography is dedicated to hiding secret messages in digital images, and has achieved the purpose of covert communication. Recently, research on image steganography has demonstrated great potential for using GAN and neural networks. In this paper we review different strategies for steganography such as cover modification, cover selection and cover synthesis by GANs, and discuss the characteristics of these methods as well as evaluation metrics and provide some possible future research directions in image steganography.
arXiv comment39 pages, 26 figures
References in corpus (25)
- Conditional Generative Adversarial Nets
- Generative Adversarial Networks: An Overview
- Improved Training of Wasserstein GANs
- Improved Techniques for Training GANs
- Energy-based Generative Adversarial Network
- BEGAN: Boundary Equilibrium Generative Adversarial Networks
- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
- A Kernel Method for the Two-Sample Problem
- Mode Regularized Generative Adversarial Networks
- Generating Steganographic Images via Adversarial Training
- An Introduction to Image Synthesis with Generative Adversarial Nets
- CycleGAN, a Master of Steganography
- Learning to Protect Communications with Adversarial Neural Cryptography
- Neural Style Transfer: A Review
- Deep Learning Face Attributes in the Wild
- Adversarial Generation of Natural Language
- SteganoGAN: High Capacity Image Steganography with GANs
- Recurrent Topic-Transition GAN for Visual Paragraph Generation
- Spatial Image Steganography Based on Generative Adversarial Network
- Revisiting Classifier Two-Sample Tests
- Coverless Information Hiding Based on Generative adversarial networks
- SSGAN: Secure Steganography Based on Generative Adversarial Networks
- Adaptive Spatial Steganography Based on Probability-Controlled Adversarial Examples
- Generative Steganography by Sampling
- Steganography Security: Principle and Practice