基于秘密信息驱动的正交GAN信息隐藏模型

doi:10.3969/j.issn.0255-8297.2019.05.013

应用科学学报 ›› 2019, Vol. 37 ›› Issue (5): 721-732.doi: 10.3969/j.issn.0255-8297.2019.05.013

基于秘密信息驱动的正交GAN信息隐藏模型

朱翌明, 陈帆, 和红杰, 陈鸿佑

西南交通大学信号与信息处理四川省重点实验室, 成都 610031

收稿日期:2019-07-27 修回日期:2019-08-01 出版日期:2019-09-30 发布日期:2019-10-18
通信作者: 和红杰,教授,博导,研究方向:信息安全、图像处理、人工智能,E-mail:hjhe@swjtu.edu.cn E-mail:hjhe@swjtu.edu.cn
基金资助:
国家自然科学基金（No.61872303，No.61461047）；四川省科技厅人才创新计划（No.2018RZ0143）；四川省科技创新创业苗子工程重点项目（No.19MZGC0163）资助

Orthogonal GAN Information Hiding Model Based on Secret Information Driven

ZHU Yiming, CHEN Fan, HE Hongjie, CHEN Hongyou

Sichuan Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China

Received:2019-07-27 Revised:2019-08-01 Online:2019-09-30 Published:2019-10-18

摘要/Abstract

摘要： 噪声驱动生成对抗网络（generative adversarial network，GAN）的生成器能生成高质量数字图像，为信息隐藏提供了新的数据载体.利用正交GAN的判别器能提取生成图像特征码的特性，提出了一种基于秘密信息驱动的正交GAN无载体信息隐藏模型.信息隐藏时，将待隐藏信息的二进制序列按分组量化规则映射为噪声向量，由该噪声向量驱动正交GAN的生成器生成含密数字图像.在信息提取时，首先利用正交GAN的判别器提取含密图像的特征码，然后利用U型网络实现从特征码到驱动噪声的映射，进而恢复秘密信息.在CelebA人脸数据集上对搭建的无载体信息隐藏模型进行对抗学习，生成器能够生成高质量的含密图像，判别器与U型网络相结合能从含密图像中提取秘密信息.与最新同类算法相比，在信息隐藏容量相同的条件下，模型具有较好的信息提取准确率、安全性等性能，同时减少了训练开销，提高了算法的实用性.

关键词: 信息隐藏, 深度学习, 生成对抗网络, U型网络

Abstract: Driven by noise, the generator of the generative adversarial network (GAN) can generate high-quality digital images and provide a new data carrier for hiding information. In this paper, a coverless information hiding model combining orthogonal GAN and Ushape network is proposed on the fact that the orthogonal GAN discriminator can extract feature codes of the generated image. While hiding information, the binary sequence of the information to be hidden is mapped into a noise vector according to a group quantization rule, and the generator of the orthogonal GAN is driven by the noise vector to generate a hidden digital image. While extracting information, the feature code of the hidden image is extracted by the discriminator of the orthogonal GAN, and then the U-shape network is used to realize the mapping from the feature code to the driving noise, thereby recovering the secret information. By performing adversarial training of the built-in model with the CelebA dataset, the generator can generate high-quality hidden images and the discriminator can be combined with the U-shaped network to extract secret information from the hidden image. Compared with the latest similar algorithms, the proposed model performs better information extraction accuracy and security under the same information hiding capacity with reduced the training overhead and improved practicability.

Key words: information hiding, deep learning, generative adversarial network(GAN), Unet

中图分类号:

TP391

朱翌明, 陈帆, 和红杰, 陈鸿佑. 基于秘密信息驱动的正交GAN信息隐藏模型[J]. 应用科学学报, 2019, 37(5): 721-732.

ZHU Yiming, CHEN Fan, HE Hongjie, CHEN Hongyou. Orthogonal GAN Information Hiding Model Based on Secret Information Driven[J]. Journal of Applied Sciences, 2019, 37(5): 721-732.

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基于秘密信息驱动的正交GAN信息隐藏模型

Orthogonal GAN Information Hiding Model Based on Secret Information Driven

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