High-capacity robust image steganography scheme based on encoding-decoding network

doi:10.11772/j.issn.1001-9081.2023040477

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (3): 772-779.DOI: 10.11772/j.issn.1001-9081.2023040477

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High-capacity robust image steganography scheme based on encoding-decoding network

Weina DONG¹^,², Jia LIU¹^,²(), Xiaozhong PAN¹^,², Lifeng CHEN¹^,², Wenquan SUN¹^,²

^1.College of Cryptography Engineering，Engineering University of PAP，Xi’an Shaanxi 710086，China
^2.Key Laboratory of Network and Information Security of PAP （Engineering University of PAP），Xi’an Shaanxi 710086，China

Received:2023-04-26 Revised:2023-07-06 Accepted:2023-07-10 Online:2023-12-04 Published:2024-03-10
Contact: Jia LIU
About author:DONG Weina， born in 1997， M. S. candidate. Her research interests include information hiding.
PAN Xiaozhong， born in 1964， Ph. D.， professor. His research interests include network and information security.
CHEN Lifeng， born in 2000， M. S. candidate. His research interests include information hiding.
SUN Wenquan， born in 2000， M. S. candidate. His research interests include information hiding.
Supported by:
National Natural Science Foundation of China(62272478)

基于编码-解码网络的大容量鲁棒图像隐写方案

董炜娜¹^,², 刘佳¹^,²(), 潘晓中¹^,², 陈立峰¹^,², 孙文权¹^,²

^1.武警工程大学密码工程学院，西安 710086
^2.网络与信息安全武警部队重点实验室（武警工程大学），西安 710086

通讯作者: 刘佳
作者简介:董炜娜（1997—），女，山东烟台人，硕士研究生，主要研究方向：信息隐藏
潘晓中（1964—），男，陕西西安人，教授，博士，主要研究方向：网络与信息安全
陈立峰（2000—），男，湖北应城人，硕士研究生，主要研究方向：信息隐藏
孙文权（2000—），男（满族），辽宁锦州人，硕士研究生，主要研究方向：信息隐藏。
基金资助:
国家自然科学基金资助项目(62272478)

Abstract

Abstract:

Aiming at the problems that the high-capacity steganography model based on encoding-decoding network has weak robustness and can not resist noise attack and channel compression， a high-capacity robust image steganography scheme based on encoding-decoding network was proposed. In the proposed scheme， encoder， decoder and discriminator based on Densely connected convolutional Network （DenseNet） were designed. The secret information and the carrier image were jointly encoded into a steganographic image by the encoder， the secret information was extracted by the decoder， and the discriminator was used to distinguish between carrier images and steganographic images. A noise layer was added between the encoder and the decoder； Dropout， JPEG compression， Gaussian blur， Gaussian noise and salt and pepper noise were used to simulate a real environment with various kinds of noise attacks. The steganographic image output by the encoder was processed by different kinds of noise and decoded by the decoder. Through training the model， the secret information could be extracted from the noise-processed steganographic image by the decoder， so that the noise attacks could be resisted. Experiment results show that the steganographic capacity of the proposed scheme reaches 0.45 - 0.95 bpp on 360×360 pixel images， and the relative embedding capacity is improved by 2.04 times compared to the suboptimal robust steganographic scheme； the decoding accuracy reaches 0.72 - 0.97， and compared with the steganography without noise layer， the average decoding accuracy is improved by 44 percentage points. The proposed scheme not only guarantees high embedding quantity and high coding image quality， but also has stronger anti-noise capability.

Key words: deep learning, information hiding, image steganography, high-capacity, robustness, encoder-decoder network, adversarial training

摘要：

针对基于编码-解码网络的大容量隐写模型存在鲁棒性弱、无法抵抗噪声攻击和信道压缩的问题，提出一种基于编码-解码网络的大容量鲁棒图像隐写方案。首先，设计了基于密集连接卷积网络（DenseNet）的编码器、解码器和判别器，编码器将秘密信息和载体图像联合编码成隐写图像，解码器提取秘密信息，判别器用于区分载体图像和隐写图像。在编码器和解码器中间加入噪声层，采用Dropout、JPEG压缩、高斯模糊、高斯噪声和椒盐噪声模拟真实环境下的各类噪声攻击，编码器输出的隐写图像经过不同种类的噪声处理，再由解码器解码；通过训练模型，解码器能够对噪声处理后的隐写图像提取秘密信息，以抵抗噪声攻击。实验结果表明，所提方案在360×360像素的图像上隐写容量达到0.45~0.95 bpp，与次优的鲁棒隐写方案相比，相对嵌入容量提升了2.04倍；解码准确率可达0.72~0.97；与未添加噪声层的隐写方案相比，平均解码准确率提高了44个百分点。所提方案在保证高嵌入量、高编码图片质量的同时具有更强的抗噪声攻击能力。

关键词: 深度学习, 信息隐藏, 图像隐写, 大容量, 鲁棒性, 编码-解码网络, 对抗性训练

CLC Number:

TP309.2

Weina DONG, Jia LIU, Xiaozhong PAN, Lifeng CHEN, Wenquan SUN. High-capacity robust image steganography scheme based on encoding-decoding network[J]. Journal of Computer Applications, 2024, 44(3): 772-779.

董炜娜, 刘佳, 潘晓中, 陈立峰, 孙文权. 基于编码-解码网络的大容量鲁棒图像隐写方案[J]. 《计算机应用》唯一官方网站, 2024, 44(3): 772-779.

Figures/Tables 15

References 39

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（λ_e，λ_d）	隐写容量/bpp	PSNR/dB	SSIM	解码准确率
（20，1）	0.95	34.27	0.95	0.97
（1，1）	0.97	25.97	0.68	0.98
（1，20）	0.99	21.21	0.49	0.99

（λ_e，λ_d）	隐写容量/bpp	PSNR/dB	SSIM	解码准确率
（20，1）	0.95	34.27	0.95	0.97
（1，1）	0.97	25.97	0.68	0.98
（1，20）	0.99	21.21	0.49	0.99

隐写方案	图像尺寸	绝对嵌入容量/b	相对嵌入容量/bpp	鲁棒性
文献［37］方案	64×64	1 634	0.400 000	否
文献［21］方案	32×32	410	0.400 000	否
文献［22］方案	64×64	300	0.070 000	否
文献［27］方案	16×16	52	0.203 000	是
文献［33］方案	128×128	8	0.000 488	是
文献［34］方案	256×256	200	0.001 020	是
文献［35］方案	128×128	64	0.003 900	是
文献［36］方案	64×64	1 280	0.313 000	是
本文方案	360×360	123 120	0.950 000	是

隐写方案	图像尺寸	绝对嵌入容量/b	相对嵌入容量/bpp	鲁棒性
文献［37］方案	64×64	1 634	0.400 000	否
文献［21］方案	32×32	410	0.400 000	否
文献［22］方案	64×64	300	0.070 000	否
文献［27］方案	16×16	52	0.203 000	是
文献［33］方案	128×128	8	0.000 488	是
文献［34］方案	256×256	200	0.001 020	是
文献［35］方案	128×128	64	0.003 900	是
文献［36］方案	64×64	1 280	0.313 000	是
本文方案	360×360	123 120	0.950 000	是

模型	攻击类别	PSNR/dB	SSIM
无噪声模型	Dropout	35.68	0.92
	JPEG压缩	35.68	0.92
	高斯模糊	35.68	0.92
	高斯噪声	30.47	0.72
	椒盐噪声	35.68	0.92
噪声模型	Dropout	34.04	0.84
	JPEG压缩	33.92	0.80
	高斯模糊	34.76	0.88
	高斯噪声	27.23	0.57
	椒盐噪声	33.83	0.89

High-capacity robust image steganography scheme based on encoding-decoding network

基于编码-解码网络的大容量鲁棒图像隐写方案

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Figures/Tables 15

References 39

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Metrics

模型	Dropout	JPEG压缩	高斯模糊	高斯噪声	椒盐噪声
无噪声模型	0.43	0.16	0.22	0.28	0.11
噪声模型	0.72	0.45	0.88	0.95	0.95

模型	Dropout	JPEG压缩	高斯模糊	高斯噪声	椒盐噪声
无噪声模型	0.71	0.58	0.61	0.64	0.55
噪声模型	0.85	0.72	0.94	0.97	0.97

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隐写方案	查看原图		不查看原图
隐写方案	隐写容量/bpp	解码准确率	隐写容量/bpp	解码准确率
文献［23］方案	0.24	0.62	0.02	0.51
本文方案	0.66	0.83	0.08	0.54

隐写方案	查看原图		不查看原图
隐写方案	隐写容量/bpp	解码准确率	隐写容量/bpp	解码准确率
文献［23］方案	0.24	0.62	0.02	0.51
本文方案	0.66	0.83	0.08	0.54