| 摘要: |
| 自适应隐写是图像隐写方向的研究热点, 它通过有效地设计隐写失真函数, 自适应地将秘密信息隐藏在图像复杂的纹理区域, 具有很强的隐蔽性. 近年来, 基于生成对抗网络的隐写失真函数设计研究在空域灰度图像上已经取得了突破性的进展, 但是目前还没有针对空域彩色图像的研究. 与灰度图像相比, 彩色图像隐写需要考虑保护RGB通道间相关性, 同时合理地分配RGB这3个通道的嵌密容量. 设计了一个基于生成对抗网络设计空域彩色图像隐写失真函数的框架CIS-GAN (color image steganography based on generative adversarial network), 生成器网络采用两个U-Net子网络结构, 第1个U-Net子网络生成修改概率矩阵, 第2个U-Net子网络进行正负向修改概率调节, 有效地降低对彩色图像通道相关性的破坏. 针对彩色图像载体, 修改灰度图像隐写分析器作为网络的对抗部分. 在生成器损失函数中对彩色图像3个通道总的隐写容量进行控制, 生成器能够自动学习分配3个通道嵌密容量. 实验结果表明, 与现有彩色图像隐写失真函数设计方法相比, 提出的网络结构能够更好地抵抗彩色图像隐写分析器的检测. |
| 关键词: 图像隐写 隐写失真函数 生成对抗网络 RGB通道相关性 |
| DOI：10.13328/j.cnki.jos.006290 |
| 分类号:TP391 |
| 基金项目:国家自然科学基金(61972142, 61872356); 国家重点研发计划(2019QY(Y)0207, 2019QY2202); 湖南省自然科学基金(2020JJ4212); 信息网络安全公安部重点实验室开放课题(C20611) |
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| Steganographic Distortion Function Design Method for Spatial Color Image Based on GAN |
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LIAO Xin1,2, TANG Zhi-Qiang1, CAO Yun2,3
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1.College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China;2.State Key Laboratory of Information Security (Institute of Information Engineering, Chinese Academy of Sciences), Beijing 100093, China;3.School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China
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| Abstract: |
| Adaptive image steganography has been becoming a hot topic, as it conceals covert information within the texture region of an image by employing a defined distortion function, which guarantees remarkable security. In spatial gray-scale image steganography, the research on designing steganographic distortion functions using generative adversarial networks has achieved a significant breakthrough recently. However, related studies of spatial color image steganography have not been reported yet so far. Compared with the gray-scale image steganography, color image steganography should preserve the RGB channel correlation and reasonably assign the embedding capacity among RGB channels simultaneously. This study first proposes a framework based on a generative adversarial network to automatically learn to generate the steganographic distortion function for spatial color images, which is termed CIS-GAN (color image steganography based on generative adversarial network). The generator is composed of two U-Net subnetworks. One of them generates the modification probability matrix, while the other adjusts the positive/negative modification probability to effectively weaken the damage to the RGB channel correlation. The analyzer of gray-scale image steganography is modified as an adversarial part of the network for color images. In addition, the generator can automatically learn to allocate the embedding capacity for the three channels via controlling the total steganographic capacity in the generator’s loss function. The experimental results show that the proposed framework outperforms the advanced steganographic schemes for spatial color images in resisting color image steganalysis. |
| Key words: image steganography steganographic distortion function generative adversarial network (GAN) RGB channel correlation |
