Coverless Information Hiding Based on Generative adversarial networks

TL;DR

This paper introduces a novel coverless image hiding method using GANs, which generates images directly from secret information, effectively resisting steganalysis and improving security over traditional steganography techniques.

Contribution

It is the first to achieve coverless information hiding with GANs, replacing image content modification with direct generation from secret data.

Findings

The method resists common steganalysis tools effectively.

It demonstrates high steganographic capacity and security.

Experimental results show good safety and reliability.

Abstract

Traditional image steganography modifies the content of the image more or less, it is hard to resist the detection of image steganalysis tools. To address this problem, a novel method named generative coverless information hiding method based on generative adversarial networks is proposed in this paper. The main idea of the method is that the class label of generative adversarial networks is replaced with the secret information as a driver to generate hidden image directly, and then extract the secret information from the hidden image through the discriminator. It's the first time that the coverless information hiding is achieved by generative adversarial networks. Compared with the traditional image steganography, this method does not modify the content of the original image. therefore, this method can resist image steganalysis tools effectively. In terms of steganographic capacity, anti-steganalysis, safety and reliability, the experimen shows that this hidden algorithm performs well.