Generative Image Steganography Based on Digital Cardan Grille

doi:10.3969/j.issn.1671-1122.2021.02.009

Abstract

Abstract:

In order to solve the problem that the carrier modification in image steganography will leave the modification trace, this paper proposes an image steganography scheme based on digital Cardan Grille. The scheme first generates the digital Cardan Grille automatically as the key of information steganography and extraction. Secondly, the blank image is used as the carrier to fill in the secret information according to the area of digital Cardan Grille. Under the condition that the secret message is kept unchanged in the whole steganography process, the damaged image of the model is generated by depth to generate the stego image with natural semantics. The results of experiments on CelebA dataset and LUSN dataset show that the new steganography scheme is feasible. Compared with other steganographic methods, this scheme is more secure in actual communication.

Key words: image steganography, digital Cardan Grille, semantic completion, GAN

CLC Number:

TP309

WANG Yaojie, YANG Xiaoyuan, LIU Wenchao. Generative Image Steganography Based on Digital Cardan Grille[J]. Netinfo Security, 2021, 21(2): 70-77.

Figures/Tables 9

References 24

[1]	ZHANG Ru, DONG Shiqi, LIU Jianyi. Invisible Steganography Via Generative Adversarial Networks[J]. Multimedia Tools and Applications, 2019,18(2): 34-39.
[2]	GOODFELLOW I, POUGET-ABADIE J, MIRZA M. Generative Adversarial Networks[EB/OL]. http://arxiv.org/abs/1406.2661, 2014-06-10.
[3]	LIU Jia, ZHOU Tianping, ZHANG Zhuo, et al. Digital Cardan Grille: A Modern Approach for Information Hiding[EB/OL]. https://arxiv.org/abs/1803.09219.pdf, 2018-03-25.
[4]	SURHONE L M, TENNOE M T, HENSSONOW S F, et al. Card Catalog (cryptology)[M]. Montana USA: Betascript Publishing, 2010.
[5]	VOLKHONSKIY D, NAZAROV I, BORISENKO B, et al. Steganographic Generative Adversarial Networks[EB/OL]. http://arxiv.org/abs/1703.05502.pdf, 2017-03-16.
[6]	WANG Yaojie, NIU Ke, YANG Xiaoyuan. Image Steganography Scheme Based on Generative Adversarial Network[J]. Journal of Computer Applications, 2018,38(10): 177-182.
	王耀杰, 钮可, 杨晓元. 基于生成对抗网络的信息隐藏方案[J]. 计算机应用, 2018,38(10): 177-182.
[7]	KE Yan, LIU Jia, ZHANG Minqing, et al. Steganography Security: Principle and Practice[J]. IEEE Access, 2018,1(99): 15-19.
[8]	FRIDRICH J. Steganography in Digital Media: Principles, Algorithms, and Applications[M]. Cambridge: Cambridge University Press, 2010.
[9]	LIU Mingming, LI Xinchao. Information Hiding Method Based on Cardan Grille[J]. Journal of Engineering University of PAP, 2018. 34(6): 58-62.
	刘明明, 李新超. 基于卡登格子的信息隐藏方法[J]. 武警工程大学学报, 2018,34(6): 58-62.
[10]	ODENA A, OLAH C, SHLENS J. Conditional Image Synjournal with Auxiliary Classifier GANs[J]. Computer Science, 2016,18(1): 14-20.
[11]	CUI Zhongli, WANG Jiazhen. Generalizable Steganography Algorithm Based on DCT Domain[J]. Computer Engineering and Design, 2006,27(13): 2449-2451.
	崔忠立, 王嘉祯. 可泛化的基于DCT域的隐写术算法[J]. 计算机工程与设计, 2006,27(13): 2449-2451.
[12]	NOVOTNY M A. Matrix Products with Applications to Classical Statistical Mechanics[J]. Journal of Biomedical Materials Research, 1978,56(3): 452-458. doi: 10.1002/1097-4636(20010905)56:3<452::aid-jbm1115>3.0.co;2-1 URL pmid: 11372064
[13]	RAGAB A H M, ISMAIL N A, ALLAH O S F. AN Efficient Message Digest Algorithm (MD) for Data Security[C]//IEEE. 2001 Tencon IEEE Region 10th International Conference on Electrical & Electronic Technology, November 8-12, 2001, Seattle, USA. New York: IEEE, 2001: 723-734.
[14]	ZHANG Hongying, PENG Qizong. Overview of Digital Image Restoration Technology[J]. Journal of Image and Graphics, 2007,1(1): 1-10.
	张红英, 彭启琮. 数字图像修复技术综述[J]. 中国图象图形学报, 2007,1(1): 1-10.
[15]	LIU Jia, KE Yan, LEI Yu, et al. The Reincarnation of Grille Cipher: A Generative Approach[EB/OL]. https://arxiv.org/abs/1804.06514.pdf, 2018-04-15.
[16]	YEH R A, CHEN C, LIM T Y, et al. Semantic Image Inpainting with Deep Generative Models[J]. IEEE Computer Society, 2016,16(7): 83-88.
[17]	RADFORD A, METZ L, CHINTALA S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[J]. Computer Science, 2015,34(8): 127-131.
[18]	HOU Xuedong. Image Translation Based on Generative Adversarial Networks[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2018.
	候学东. 基于生成对抗网络的图像翻译[D]. 南京: 南京邮电大学, 2018.
[19]	AMOS B. Image Completion with Deep Learning in TensorFlow[EB/OL]. https://github.com/bamos/dcgan-completion.tensorflow, 2018-09-23.
[20]	MANDGAONKAR V S, KULKARNI C V. No Reference Image Quality Assessment[C]// IEEE. 2014 Annual IEEE India Conference (INDICON), November 8-12, 2014, New York, USA. New York: IEEE, 2015: 1241-1248.
[21]	SURYAWANSHI G R, MALI S N. Study of Effect of DCT Domain Steganography Techniques in Spatial Domain for JPEG Images Steganalysis[J]. International Journal of Computer Applications, 2015,127(6): 16-20.
[22]	ZHEN Yu, CHEN Kefei. Analysis and Improvement on RS Detection Algorithm[J]. Computer Engineering, 2008,34(8): 168-170.
[23]	YAN Huijuan, QIN Jie. Trojan Horse Detection Method Based on Nonlinear SVM Model[J]. Computer Engineering, 2011,11(2): 134-137.
[24]	CHEN Tao, LU Shijian, FAN Jiayuan. S-CNN: Subcategory-aware Convolutional Networks for Object Detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017,16(4): 12-17.

软件平台	Tensorflow v0.12
硬件环境	CPU	i7-8250U 3.2 GHz
	RAM	16GB DDR3 1600 MHz
	GPU	NVIDIA 1080

检测算法	检测准确率
检测算法	分组准确率	平均准确率
DCT域隐写分析	0.534	0.556
DCT域隐写分析	0.579	0.556
RS检测	0.510	0.516
RS检测	0.523	0.516
非线性SVM检测	0.573	0.562
非线性SVM检测	0.551	0.562
S-CNN检测	0.672	0.658
S-CNN检测	0.644	0.658