基于轮廓自动生成的构造式图像隐写方法

doi:10.11959/j.issn.1000-436x.2021174

Abstract

Abstract:

To address the problems of limited hiding capacity and inaccurate information extraction in the existing generative steganography methods, a novel generative steganography method was proposed based on auto-generation of contours, which consisted of two main stages, such as the contour generation driven by secret information and the contour-to-image transformation.Firstly, the contour generation model was built based on long short term memory (LSTM) for secret information-driven auto-generation of object contours.Then, a contour-to-image reversible transformation model was constructed based on pix2pix network to obtain the stego-image, and the model also supported the reversible transformations from the stego-image to contours for secret information extraction.Experimental results demonstrate that the proposed method not only achieves high hiding capacity and accurate information extraction simultaneously, but also effectively resists the attacks by steganalysis tools.It performs much better than the state-of-the-art generative steganographic methods.

Key words: generative steganography, coverless information hiding, deep learning, generative adversarial network

CLC Number:

TN911.73

Zhili ZHOU, Meimin WANG, Gaobo YANG, Jianyu ZHU, Xingming SUN. Generative steganography method based on auto-generation of contours[J]. Journal on Communications, 2021, 42(9): 144-154.

Figures/Tables 13

Bit	本文方法	SWE方法
2×256	$0 . 0127$	0.071 9
4×256	$0 . 0145$	0.073 2
6×256	$0 . 0176$	0.079 4
8×256	$0 . 0198$	0.080 3

隐写分析器	方法	Bit
隐写分析器	方法	2×256	4×256	6×256	8×256
	S-UNIWARD	0.455 8	0.452 1	0.448 9	0.441 2
SRM	UT-6HPF-GAN	0.461 7	0.465 3	0.457 6	0.451 2
	SWE方法	0.478 9	0.473 8	0.476 4	0.473 2
	本文方法	$0 . 4934$	$0 . 4902$	$0 . 4896$	$0 . 4921$
	S-UNIWARD	0.457 9	0.454 3	0.456 7	0.450 1
XuNet	UT-6HPF-GAN	0.463 4	0.460 9	0.468 9	0.453 5
	SWE方法	0.481 3	0.481 8	0.486 4	0.487 6
	本文方法	$0 . 4988$	$0 . 4995$	$0 . 5011$	$0 . 5002$

Bit	SWE方法	本文方法
2×256	404.609 6	$77 . 4492$
4×256	401.378 1	$84 . 7755$
6×256	422.920 3	$89 . 7363$
8×256	437.523 9	$91 . 9796$

References 29

[1]	FILLER T , JUDAS J , FRIDRICH J . Minimizing additive distortion in steganography using syndrome-trellis codes[J]. IEEE Transactions on Information Forensics and Security, 2011,6(3): 920-935.
[2]	LIAO X , YU Y B , LI B ,et al. A new payload partition strategy in color image steganography[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020,30(3): 685-696.
[3]	付章杰, 王帆, 孙星明 ,等. 基于深度学习的图像隐写方法研究[J]. 计算机学报, 2020,43(9): 1656-1672.
	FU Z J , WANG F , SUN X M ,et al. Research on steganography of dig-ital images based on deep learning[J]. Chinese Journal of Computers, 2020,43(9): 1656-1672.
[4]	FRIDRICH J , GOLJAN M , DU R . Detecting LSB steganography in color,and gray-scale images[J]. IEEE MultiMedia, 2001,8(4): 22-28.
[5]	HOLUB V , FRIDRICH J , DENEMARK T . Universal distortion function for steganography in an arbitrary domain[J]. EURASIP Journal on Information Security, 2014,2014(1): 1-13.
[6]	LI B , WANG M , HUANG J W ,et al. A new cost function for spatial image steganography[C]// 2014 IEEE International Conference on Image Processing (ICIP). Piscataway:IEEE Press, 2014: 4206-4210.
[7]	PEVNY T , FILLER T , BAS P . Using high-dimensional image models to perform highly undetectable steganography[C]// International Workshop on Information Hiding. Berlin:Springer, 2010: 161-177.
[8]	HOLUB V , FRIDRICH J . Designing steganographic distortion using directional filters[C]// 2012 IEEE International Workshop on Information Forensics and Security (WIFS). Piscataway:IEEE Press, 2012: 234-239.
[9]	YANG J H , RUAN D Y , HUANG J W ,et al. An embedding cost learning framework using GAN[J]. IEEE Transactions on Information Forensics and Security, 2020,15: 839-851.
[10]	GOODFELLOW I , POUGET-ABADIE J ,, MIRZA M ,et al. Generative adversarial networks[J]. Communications of the ACM, 2020,63(11): 139-144.
[11]	TANG W X , LI B , BARNI M ,et al. An automatic cost learning framework for image steganography using deep reinforcement learning[J]. IEEE Transactions on Information Forensics and Security, 2021,16: 952-967.
[12]	DUAN X T , JIA K , LI B X ,et al. Reversible image steganography scheme based on a U-Net structure[J]. IEEE Access, 2019,7: 9314-9323.
[13]	FRIDRICH J , KODOVSKY J . Rich models for steganalysis of digital images[J]. IEEE Transactions on Information Forensics and Security, 2012,7(3): 868-882.
[14]	XU G S , WU H Z , SHI Y Q . Structural design of convolutional neural networks for steganalysis[J]. IEEE Signal Processing Letters, 2016,23(5): 708-712.
[15]	张新鹏, 钱振兴, 李晟 . 信息隐藏研究展望[J]. 应用科学学报, 2016,34(5): 475-489.
	ZHANG X P , QIAN Z X , LI S . Prospect of digital steganography re-search[J]. Journal of Applied Sciences, 2016,34(5): 475-489.
[16]	ZHOU Z L , SUN H Y , HARIT R ,et al. Coverless image steganography without embedding[C]// International Conference on Cloud Computing and Security. Berlin:Springer, 2015: 123-132.
[17]	周志立, 曹燚, 孙星明 . 基于图像 bag-of-words 模型的无载体信息隐藏[J]. 应用科学学报, 2016,34(5): 527-536.
	ZHOU Z L , CAO Y , SUN X M . Coverless information hiding based on bag-of-words model of image[J]. Journal of Applied Sciences, 2016,34(5): 527-536.
[18]	WU K C , WANG C M . Steganography using reversible texture synthesis[J]. IEEE Transactions on Image Processing, 2015,24(1): 130-139.
[19]	QIAN Z X , ZHOU H , ZHANG W M ,et al. Robust steganography using texture synthesis[M]. Berlin: Springer, 2017.
[20]	MEGíAS D . Improved privacy-preserving P2P multimedia distribution based on recombined fingerprints[J]. IEEE Transactions on Dependable and Secure Computing, 2015,12(2): 179-189.
[21]	LI S , ZHANG X P . Toward construction-based data hiding:from secrets to fingerprint images[J]. IEEE Transactions on Image Processing, 2019,28(3): 1482-1497.
[22]	刘明明, 张敏情, 刘佳 ,等. 基于生成对抗网络的无载体信息隐藏[J]. 应用科学学报, 2018,36(2): 371-382.
	LIU M M , ZHANG M Q , LIU J ,et al. Coverless information hiding based on generative adversarial networks[J]. Journal of Applied Sciences, 2018,36(2): 371-382.
[23]	HU D H , WANG L , JIANG W J ,et al. A novel image steganography method via deep convolutional generative adversarial networks[J]. IEEE Access, 2018,6: 38303-38314.
[24]	HOCHREITER S , SCHMIDHUBER J . Long short-term memory[J]. Neural Computation, 1997,9(8): 1735-1780.
[25]	YANG Z L , GUO X Q , CHEN Z M ,et al. RNN-stega:linguistic steganography based on recurrent neural networks[J]. IEEE Transactions on Information Forensics and Security, 2019,14(5): 1280-1295.
[26]	MIRZA M , OSINDERO S . Conditional generative adversarial nets[J]. arXiv Preprint,arXiv:1411.1784, 2014.
[27]	ISOLA P , ZHU J Y , ZHOU T H ,et al. Image-to-image translation with conditional adversarial networks[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2017: 5967-5976.
[28]	RONNEBERGER O , FISCHER P , BROX T . U-Net:convolutional networks for biomedical image segmentation[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin:Springer, 2015: 234-241.
[29]	CANNY J . A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986,8(6): 679-698.

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λ	μ =1		μ=50		μ=100
λ	BER	EMD	BER	EMD	BER	EMD
1	0.015 8	0.027 8	0.015 4	0.029 1	0.015 5	0.029 6
50	0.016 1	0.026 1	0.015 7	0.028 1	0.015 8	0.028 7
100	0.017 2	0.026 2	0.016 2	0.027 9	0.015 8	0.026 9

l	Bit	BER	EMD
2	2×256	0.012 7	0.020 5
4	4×256	0.014 5	0.027 0
6	6×256	0.017 6	0.026 6
8	8×256	0.019 8	0.055 5

方法	生成模型		生成器		判别器		提取器		BER	P_E	EMD
方法	LSTM	RNN	U-Net	Encoder-decoder	PatchGAN	GAN	U-Net	Canny算子	BER	P_E	EMD
方法1	×	√	√	×	√	×	√	×	0.015 1	0.421 3	0.184 4
方法2	√	×	×	√	√	×	√	×	0.048 2	0.488 1	0.049 7
方法3	√	×	√	×	×	√	√	×	0.019 4	0.481 5	0.031 9
方法4	√	×	√	×	√	×	×	√	0.093 2	0.489 6	0.027 7
本文方法	√	×	√	×	√	×	√	×	0.014 5	0.490 2	0.026 0
注：√表示采用此结构，×表示不采用此结构。

Generative steganography method based on auto-generation of contours

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