一种基于GAN和纠错编码技术的无嵌入隐写方法

为解决传统的信息隐藏技术中隐写容量小和隐写安全性低的不足,提出了利用生成式对抗网络(GAN)的无载体信息隐藏方法.首先利用噪声驱动生成器直接生成含密图像,然后训练秘密信息提取器以恢复隐藏的秘密消息.同时,进一步优化了提取器的训练任务,并引入冗余纠错编码技术.实验结果表明,相比同类方法,在大隐写容量的情况下,具有更高的信息提取准确率,同时加快了提取器的训练收敛速度.

A Steganography Method Without Embedding Based on GAN and Error-correcting Code Technology

A coverless information hiding method using Generative Adversarial Network (GAN) is proposed to address the shortcomings of the traditional information hiding techniques in terms of small hiding capacity and low security.Firstly, a generator is used to generate the digital imagedriven by the secret data directly. Next, the extractor for secret is trained to extracthidden information.At the same time, the training task of the extractor is further optimized and redundant error correction coding techniques are introduced. Compared with similar algorithms,at high steganography capacity,the proposed method ensures higher information extraction accuracy andfasterthe convergence speed intrainingstageofextractor.