Color image information hiding algorithm based on style transfer process

doi:10.11772/j.issn.1001-9081.2022060953

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1730-1735.DOI: 10.11772/j.issn.1001-9081.2022060953

Special Issue: CCF第37届中国计算机应用大会 (CCF NCCA 2022)

• The 37 CCF National Conference of Computer Applications (CCF NCCA 2022) • Previous Articles Next Articles

Color image information hiding algorithm based on style transfer process

Pan YANG¹^,², Minqing ZHANG¹^,²(), Yu GE¹^,², Fuqiang DI¹^,², Yingnan ZHANG¹^,²

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

Received:2022-07-02 Revised:2022-08-12 Accepted:2022-08-16 Online:2022-09-23 Published:2023-06-10
Contact: Minqing ZHANG
About author:YANG Pan， born in 1994， M. S. candidate. His research interests include information hiding， deep learning.
GE Yu， born in 1998， M. S. candidate. Her research interests include information hiding， information security.
DI Fuqiang， born in 1990， Ph. D.， lecturer. His research interests include information hiding， information security.
ZHANG Yingnan， born in 1990， Ph. D.， associate professor. His research interests include information hiding， information security.
Supported by:
National Natural Science Foundation of China(61872384);Basic Frontier Research Fund of Engineering University of PAP(WJY202012)

基于风格迁移过程的彩色图像信息隐藏算法

杨盼¹^,², 张敏情¹^,²(), 葛虞¹^,², 狄富强¹^,², 张英男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: 张敏情
作者简介:杨盼（1994—），男，河南洛阳人，硕士研究生，主要研究方向：信息隐藏、深度学习
葛虞（1998—），女，河北秦皇岛人，硕士研究生，主要研究方向：信息隐藏、信息安全
狄富强（1990—），男，陕西西安人，讲师，博士，主要研究方向：信息隐藏、信息安全
张英男（1990—），男，陕西西安人，副教授，博士，主要研究方向：信息隐藏、信息安全。
基金资助:
国家自然科学基金资助项目(61872384);武警工程大学基础前沿研究基金资助项目(WJY202012)

Abstract

Abstract:

To solve the problem that information hiding algorithms based on neural style transfer do not solve the embedding problem of color images， a color image information hiding algorithm based on style transfer process was proposed. Firstly， the advantages of feature extraction of Convolutional Neural Network （CNN） were utilized to extract the semantic information of the carrier image， the style information of the style image and the feature information of the color image， respectively. Then， the semantic content of images and different styles were fused together. Finally the embedding of color image was completed while performing the style transfer of the carrier image through the decoder. Experimental results show that the proposed algorithm can integrate the secret image into the generated stylized image effectively， making the secret information embedding behavior indistinguishable from the style change behavior. Under the premise of maintaining the security of the algorithm， the proposed algorithm has the hiding capacity increased to 24 bpp， and the average values of Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） reached 25.29 dB and 0.85 respectively， thereby solving the color image embedding problem effectively.

Key words: information hiding, neural network, style transfer, high capacity, image quality

摘要：

针对基于神经风格迁移的信息隐藏算法没有解决彩色图像的嵌入这一问题，提出了一种基于风格迁移过程的彩色图像信息隐藏算法。首先，利用卷积神经网络（CNN）特征提取的优势，分别提取载体图像的语义信息、风格图像的风格信息以及彩色图像的特征信息；然后，将图像的语义内容和不同风格融合在一起；最后，在通过解码器对载体图像进行风格迁移的同时完成彩色图像的嵌入。实验结果表明，所提算法可以将秘密图像有效融入到生成的风格化图像中，使得秘密信息嵌入行为与风格变换的行为不可区分，在保持算法安全性的前提下，所提算法的隐藏容量提高到24 bpp，峰值信噪比（PSNR）和结构相似性（SSIM）的平均值分别达到了25.29 dB和0.85，有效解决了彩色图像的嵌入问题。

关键词: 信息隐藏, 神经网络, 风格迁移, 高容量, 图像质量

CLC Number:

TP309.2

Pan YANG, Minqing ZHANG, Yu GE, Fuqiang DI, Yingnan ZHANG. Color image information hiding algorithm based on style transfer process[J]. Journal of Computer Applications, 2023, 43(6): 1730-1735.

杨盼, 张敏情, 葛虞, 狄富强, 张英男. 基于风格迁移过程的彩色图像信息隐藏算法[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1730-1735.

Figures/Tables 10

References 24

1	PEVNÝ T， FILLER T， BAS P. Using high-dimensional image models to perform highly undetectable steganography［C］// Proceedings of the 2010 International Workshop on Information Hiding， LNCS 6387. Berlin： Springer， 2010： 161-177.
2	HOLUB V， FRIDRICH J. Designing steganographic distortion using directional filters［C］// Proceedings of the 2012 IEEE International Workshop on Information Forensics and Security. Piscataway： IEEE， 2012： 234-239. 10.1109/wifs.2012.6412655
3	LI B， TAN S Q， WANG M， et al. Investigation on cost assignment in spatial image steganography［J］. IEEE Transactions on Information Forensics and Security， 2014， 9（8）： 1264-1277. 10.1109/tifs.2014.2326954
4	HOLUB V， FRIDRICH J， DENEMARK T. Universal distortion function for steganography in an arbitrary domain［J］. EURASIP Journal on Information Security， 2014， 2014： No.1. 10.1186/1687-417x-2014-1
5	FRIDRICH J， KODOVSKÝ J. Rich models for steganalysis of digital images ［J］. IEEE Transactions on Information Forensics and Security， 2012， 7（3）： 868-882. 10.1109/tifs.2012.2190402
6	BALUJA S. Hiding images within images ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2020， 42（7）： 1685-1697. 10.1109/tpami.2019.2901877
7	BALUJA S. Hiding images in plain sight： deep steganography ［C］// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook， NY： Curran Associates Inc.， 2017： 2066-2076.
8	REHMAN A UR， RAHIM R， NADEEM S， et al. End-to-end trained CNN encoder-decoder networks for image steganography［C］// Proceedings of the 2018 European Conference on Computer Vision， LNCS 11132. Cham： Springer， 2019： 723-729.
9	ZHANG R， DONG S Q， LIU J Y. Invisible steganography via generative adversarial networks［J］. Multimedia Tools and Applications， 2019， 78（7）： 8559-8575. 10.1007/s11042-018-6951-z
10	GATYS L A， ECKER A S， BETHGE M. Image style transfer using convolutional neural networks ［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 2414-2423. 10.1109/cvpr.2016.265
11	SIMONYAN K， ZISSERMAN A. Very deep convolutional networks for large-scale image recognition［EB/OL］. （2015-04-10）［2022-04-14］..
12	HUANG X， BELONGIE S. Arbitrary style transfer in real-time with adaptive instance normalization ［C］// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway： IEEE， 2017： 1510-1519. 10.1109/iccv.2017.167
13	LI X T， LIU S F， KAUTZ J， et al. Learning linear transformations for fast image and video style transfer ［C］// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2019： 3804-3812. 10.1109/cvpr.2019.00393
14	LI Y J， FANG C， YANG J M， et al. Universal style transfer via feature transforms［C］// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook， NY： Curran Associates Inc.， 2017： 385-395.
15	AN J， HUANG S Y， SONG Y B， et al. ArtFlow： unbiased image style transfer via reversible neural flows［C］// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2021： 862-871. 10.1109/cvpr46437.2021.00092
16	WANG Z H， GAO N， WANG X， et al. STNet： a style transformation network for deep image steganography ［C］// Proceedings of the 2019 International Conference on Neural Information Processing， LNCS 11954. Cham： Springer， 2019： 3-14.
17	BI X L， YANG X Y， WANG C， et al. High-capacity image steganography algorithm based on image style transfer［J］. Security and Communication Networks， 2021， 2021： No.4179340. 10.1155/2021/4179340
18	LIN W J， ZHU X K， YE W J， et al. An improved image steganography framework based on Y channel information for neural style transfer［J］. Security and Communication Networks， 2022， 2022： No.2641615. 10.1155/2022/2641615
19	WANG Z， SIMONCELLI E P， BOVIK A C. Multiscale structural similarity for image quality assessment［C］// Proceedings of the 37th Asilomar Conference on Signals， Systems and Computers. Piscataway： IEEE， 2003，2： 1398-1402. 10.1109/acssc.2003.1292181
20	BOEHM B. StegExpose — a tool for detecting LSB steganography［EB/OL］. （2014-10-24）［2022-05-01］..
21	DUMITRESCU S， WU X L， WANG Z. Detection of LSB steganography via sample pair analysis ［J］. IEEE Transactions on Signal Processing， 2003， 51（7）： 1995-2007. 10.1109/tsp.2003.812753
22	FRIDRICH J， GOLJAN M， DU R. Reliable detection of LSB steganography in color and grayscale images ［C］// Proceedings of the 2001 Workshop on Multimedia and Security： New Challenges. New York： ACM， 2001： 27-30. 10.1145/1232454.1232466
23	WESTFELD A， PFITZMANN A. Attacks on steganographic systems ［C］// Proceedings of the 1999 International Workshop on Information Hiding， LNCS 1768. Berlin： Springer， 2000： 61-76.
24	付章杰，李恩露，程旭，等. 基于深度学习的图像隐写研究进展［J］. 计算机研究与发展， 2021， 58（3）：548-568. 10.7544/issn1000-1239.2021.20200360
	FU Z J， LI E L， CHENG X， et al. Recent advances in image steganography based on deep learning ［J］. Journal of Computer Research and Development， 2021， 58（3）：548-568. 10.7544/issn1000-1239.2021.20200360

卷积层	输入	卷积核	步长	输出
Conv1	3×256×256	3×3	1	32×256×256
Conv2	32×256×256	3×3	1	64×256×256
Conv3	64×256×256	3×3	1	128×256×256
Conv4	128×256×256	3×3	1	256×256×256
Conv5	256×256×256	3×3	1	128×256×256
Conv6	128×256×256	3×3	1	64×256×256
Conv7	64×256×256	3×3	1	32×256×256
Conv8	32×256×256	3×3	1	3×256×256

卷积层	输入	卷积核	步长	输出
Conv1	3×256×256	3×3	1	32×256×256
Conv2	32×256×256	3×3	1	64×256×256
Conv3	64×256×256	3×3	1	128×256×256
Conv4	128×256×256	3×3	1	256×256×256
Conv5	256×256×256	3×3	1	128×256×256
Conv6	128×256×256	3×3	1	64×256×256
Conv7	64×256×256	3×3	1	32×256×256
Conv8	32×256×256	3×3	1	3×256×256

算法	秘密信息	载体图像	容量/bpp
文献［9］算法	256×256×1	256×256×3	8.00
文献［8］算法	256×256×1	256×256×3	8.00
文献［7］算法	200×200×3	200×200×3	24.00
文献［16］算法	任意长度	任意大小	0.06
文献［17］算法	256×256×1	256×256×3	8.00
文献［18］算法	256×256×1	256×256×3	8.00
本文算法	256×256×3	256×256×3	24.00

算法	秘密信息	载体图像	容量/bpp
文献［9］算法	256×256×1	256×256×3	8.00
文献［8］算法	256×256×1	256×256×3	8.00
文献［7］算法	200×200×3	200×200×3	24.00
文献［16］算法	任意长度	任意大小	0.06
文献［17］算法	256×256×1	256×256×3	8.00
文献［18］算法	256×256×1	256×256×3	8.00
本文算法	256×256×3	256×256×3	24.00

算法	PSNR/dB	SSIM
文献［9］算法	36.58	0.95
文献［8］算法	36.59	0.96
文献［7］算法	37.60	0.97
文献［17］算法	30.48	0.75
文献［18］算法	24.14	0.81
本文算法	25.29	0.85

Color image information hiding algorithm based on style transfer process

基于风格迁移过程的彩色图像信息隐藏算法

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 24

Related Articles 15

Recommended Articles

Metrics

[1]	Guanglei YAO, Juxia XIONG, Guowu YANG. Flower pollination algorithm based on neural network optimization [J]. Journal of Computer Applications, 2024, 44(9): 2829-2837.
[2]	Ying HUANG, Jiayu YANG, Jiahao JIN, Bangrui WAN. Siamese mixed information fusion algorithm for RGBT tracking [J]. Journal of Computer Applications, 2024, 44(9): 2878-2885.
[3]	Yu DU, Yan ZHU. Constructing pre-trained dynamic graph neural network to predict disappearance of academic cooperation behavior [J]. Journal of Computer Applications, 2024, 44(9): 2726-2731.
[4]	Xingyao YANG, Yu CHEN, Jiong YU, Zulian ZHANG, Jiaying CHEN, Dongxiao WANG. Recommendation model combining self-features and contrastive learning [J]. Journal of Computer Applications, 2024, 44(9): 2704-2710.
[5]	Na WANG, Lin JIANG, Yuancheng LI, Yun ZHU. Optimization of tensor virtual machine operator fusion based on graph rewriting and fusion exploration [J]. Journal of Computer Applications, 2024, 44(9): 2802-2809.
[6]	Yun LI, Fuyou WANG, Peiguang JING, Su WANG, Ao XIAO. Uncertainty-based frame associated short video event detection method [J]. Journal of Computer Applications, 2024, 44(9): 2903-2910.
[7]	Tingjie TANG, Jiajin HUANG, Jin QIN. Session-based recommendation with graph auxiliary learning [J]. Journal of Computer Applications, 2024, 44(9): 2711-2718.
[8]	Rui ZHANG, Pengyun ZHANG, Meirong GAO. Self-optimized dual-modal multi-channel non-deep vestibular schwannoma recognition model [J]. Journal of Computer Applications, 2024, 44(9): 2975-2982.
[9]	Jinjin LI, Guoming SANG, Yijia ZHANG. Multi-domain fake news detection model enhanced by APK-CNN and Transformer [J]. Journal of Computer Applications, 2024, 44(9): 2674-2682.
[10]	Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network [J]. Journal of Computer Applications, 2024, 44(9): 2970-2974.
[11]	Hang YANG, Wanggen LI, Gensheng ZHANG, Zhige WANG, Xin KAI. Multi-layer information interactive fusion algorithm based on graph neural network for session-based recommendation [J]. Journal of Computer Applications, 2024, 44(9): 2719-2725.
[12]	Hong CHEN, Bing QI, Haibo JIN, Cong WU, Li’ang ZHANG. Class-imbalanced traffic abnormal detection based on 1D-CNN and BiGRU [J]. Journal of Computer Applications, 2024, 44(8): 2493-2499.
[13]	Ying YANG, Xiaoyan HAO, Dan YU, Yao MA, Yongle CHEN. Graph data generation approach for graph neural network model extraction attacks [J]. Journal of Computer Applications, 2024, 44(8): 2483-2492.
[14]	Zheyuan SHEN, Keke YANG, Jing LI. Personalized federated learning method based on dual stream neural network [J]. Journal of Computer Applications, 2024, 44(8): 2319-2325.
[15]	Chunxue ZHANG, Liqing QIU, Cheng’ai SUN, Caixia JING. Purchase behavior prediction model based on two-stage dynamic interest recognition [J]. Journal of Computer Applications, 2024, 44(8): 2365-2371.