Journal on Communications ›› 2021, Vol. 42 ›› Issue (9): 144-154.doi: 10.11959/j.issn.1000-436x.2021174
• Papers • Previous Articles Next Articles
Zhili ZHOU1,2, Meimin WANG1,2, Gaobo YANG3, Jianyu ZHU1,2, Xingming SUN1,2
Revised:
2021-08-09
Online:
2021-09-25
Published:
2021-09-01
Supported by:
CLC Number:
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.
"
隐写分析器 | 方法 | Bit | |||
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 | |||||
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 |
"
方法 | 生成模型 | 生成器 | 判别器 | 提取器 | BER | PE | EMD | |||||||
LSTM | RNN | U-Net | Encoder-decoder | PatchGAN | GAN | U-Net | Canny算子 | |||||||
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。 |
[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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