Journal on Communications ›› 2022, Vol. 43 ›› Issue (5): 68-81.doi: 10.11959/j.issn.1000-436x.2022089
• Papers • Previous Articles Next Articles
Xiaodan WANG, Jingtai LI, Yafei SONG
Revised:
2022-03-23
Online:
2022-05-25
Published:
2022-05-01
Supported by:
CLC Number:
Xiaodan WANG, Jingtai LI, Yafei SONG. DDAC: a feature extraction method for model of image steganalysis based on convolutional neural network[J]. Journal on Communications, 2022, 43(5): 68-81.
"
结构 | WOW | S-UNIWARD | |||
嵌入率为0.2 | 嵌入率为0.4 | 嵌入率为0.2 | 嵌入率为0.4 | ||
SRM方法 | 0.738 3 | 0.844 2 | 0.691 3 | 0.803 1 | |
结构1 | 0.775 9 | 0.870 3 | 0.718 3 | 0.845 2 | |
结构2 | 0.761 5 | 0.845 1 | 0.708 3 | 0.835 4 | |
结构3 | 0.756 9 | 0.857 7 | 0.689 2 | 0.823 4 | |
结构4 | 0.730 8 | 0.832 0 | 0.695 1 | 0.820 7 | |
结构5 | 0.732 7 | 0.846 1 | 0.685 0 | 0.810 4 | |
结构6 | 0.730 5 | 0.831 2 | 0.689 7 | 0.808 9 |
"
模型 | WOW | S-UNIWARD | |||
嵌入率为0.4 | 嵌入率为0.2 | 嵌入率为0.4 | 嵌入率为0.2 | ||
Ye-net | 0.791 8 | 0.674 9 | 0.743 2 | 0.629 6 | |
Ye-net+DDAC | 0.813 5 | 0.708 1 | 0.764 5 | 0.642 7 | |
Yedroudj-net | 0.857 3 | 0.749 5 | 0.754 0 | 0.626 4 | |
Yedroudj-net+DDAC | 0.870 3 | 0.774 3 | 0.836 1 | 0.701 9 | |
SRnet | 0.869 3 | 0.754 0 | 0.816 9 | 0.675 8 | |
SiaStegnet | 0.870 1 | 0.760 3 | 0.821 3 | 0.684 3 | |
Zhu-net | 0.881 6 | 0.767 1 | 0.847 3 | 0.715 0 | |
Hybrid-CNN | 0.774 0 | 0.547 0 | 0.919 0 | 0.503 0 | |
DDAC-net | 0.875 0 | 0.785 8 | 0.854 8 | 0.711 8 |
[1] | CHEDDAD A , CONDELL J , CURRAN K ,et al. Digital image steganography:survey and analysis of current methods[J]. Signal Processing, 2010,90(3): 727-752. |
[2] | 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 digital images based on deep learning[J]. Chinese Journal of Computers, 2020,43(9): 1656-1672. | |
[3] | 陈君夫, 付章杰, 张卫明 ,等. 基于深度学习的图像隐写分析综述[J]. 软件学报, 2021,32(2): 551-578. |
CHEN J F , FU Z J , ZHANG W M ,et al. Review of image steganalysis based on deep learning[J]. Journal of Software, 2021,32(2): 551-578. | |
[4] | LECUN Y , BENGIO Y , HINTON G . Deep learning[J]. Nature, 2015,521(7553): 436-444. |
[5] | KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017,60(6): 84-90. |
[6] | HE K M , ZHANG X Y , REN S Q ,et al. Deep residual learning for image recognition[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 770-778. |
[7] | RONNEBERGER O , FISCHER P , BROX T . U-net:convolutional networks for biomedical image segmentation[C]// Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. Berlin:Springer, 2015: 234-241. |
[8] | HE K M , ZHANG X Y , REN S Q ,et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,37(9): 1904-1916. |
[9] | GOODFELLOW I J , POUGET-ABADIE J ,, MIRZA M , et al . Generative adversarial nets[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge:MIT Press, 2014: 2672-2680. |
[10] | 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. |
[11] | TANG W X , TAN S Q , LI B ,et al. Automatic steganographic distortion learning using a generative adversarial network[J]. IEEE Signal Processing Letters, 2017,24(10): 1547-1551. |
[12] | KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017,60(6): 84-90. |
[13] | TAN S Q , LI B . Stacked convolutional auto-encoders for steganalysis of digital images[C]// Proceedings of Signal and Information Processing Association Annual Summit and Conference (APSIPA). Piscataway:IEEE Press, 2014: 1-4. |
[14] | QIAN Y L , DONG J , WANG W ,et al. Deep learning for steganalysis via convolutional neural networks[C]// Proceedings of SPIE - The International Society for Optical Engineering. Bellingham:SPIE Press, 2015: 171-180. |
[15] | 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. |
[16] | YEDROUDJ M , COMBY F , CHAUMONT M . Yedroudj-net:an efficient CNN for spatial steganalysis[C]// Proceedings of 2018 IEEE International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2018: 2092-2096. |
[17] | YE J , NI J Q , YI Y . Deep learning hierarchical representations for image steganalysis[J]. IEEE Transactions on Information Forensics and Security, 2017,12(11): 2545-2557. |
[18] | FRIDRICH J , KODOVSKY J . Rich models for steganalysis of digital images[J]. IEEE Transactions on Information Forensics and Security, 2012,7(3): 868-882. |
[19] | LI B , WEI W H , FERREIRA A ,et al. ReST-net:diverse activation modules and parallel subnets-based CNN for spatial image steganalysis[J]. IEEE Signal Processing Letters, 2018,25(5): 650-654. |
[20] | KALMAN B L , KWASNY S C . Why tanh:choosing a sigmoidal function[C]// Proceedings of International Joint Conference on Neural Networks. Piscataway:IEEE Press, 1992: 578-581. |
[21] | BOROUMAND M , CHEN M , FRIDRICH J . Deep residual network for steganalysis of digital images[J]. IEEE Transactions on Information Forensics and Security, 2019,14(5): 1181-1193. |
[22] | SZEGEDY C , IOFFE S , VANHOUCKE V ,et al. Inception-v4,inception-ResNet and the impact of residual connections on learning[C]// Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. New York:ACM Press, 2017: 4278-4284. |
[23] | ZHANG R , ZHU F , LIU J Y ,et al. Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis[J]. IEEE Transactions on Information Forensics and Security, 2020,15: 1138-1150. |
[24] | PEVNY T , BAS P , FRIDRICH J . Steganalysis by subtractive pixel adjacency matrix[J]. IEEE Transactions on Information Forensics and Security, 2010,5(2): 215-224. |
[25] | SUTSKEVER I , MARTENS J , DAHL G ,et al. On the importance of initialization and momentum in deep learning[C]// International Conference on Machine Learning. Saarland:DBLP, 2013: 1139-1147. |
[26] | IOFFE S , SZEGEDY C . Batch normalization:accelerating deep network training by reducing internal covariate shift[C]// Proceedings of the 32nd International Conference on Machine Learning. Saarland:DBLP, 2015: 448-456. |
[27] | NAIR V , HINTON G E . Rectified linear units improve restricted boltzmann machines[C]// Proceedings of the 27th International Conference on Machine Learning. Saarland:DBLP, 2010: 807-814. |
[28] | BAS P , FILLER T , PEVNY T , . “Break our steganographic system”:the ins and outs of organizing BOSS[C]// Information Hiding. Berlin:Springer, 2011: 59-70. |
[29] | KODOVSKY J , FRIDRICH J , HOLUB V . Ensemble classifiers for steganalysis of digital media[J]. IEEE Transactions on Information Forensics and Security, 2012,7(2): 432-444. |
[30] | 沈军, 廖鑫, 秦拯 ,等. 基于卷积神经网络的低嵌入率空域隐写分析[J]. 软件学报, 2021,32(9): 2901-2915. |
SHEN J , LIAO X , QIN Z ,et al. Spatial steganalysis of low embedding rate based on convolutional neural network[J]. Journal of Software, 2021,32(9): 2901-2915. | |
[31] | YOU W K , ZHANG H , ZHAO X F . A siamese CNN for image steganalysis[J]. IEEE Transactions on Information Forensics and Security, 2021,16: 291-306. |
[32] | ARIVAZHAGAN S , AMRUTHA E , SYLVIA L J W ,et al. Hybrid convolutional neural network architecture driven by residual features for steganalysis of spatial steganographic algorithms[J]. Neural Computing and Applications, 2021,33(17): 11465-11485. |
[33] | QIAN Y L , DONG J , WANG W ,et al. Learning and transferring representations for image steganalysis using convolutional neural network[C]// Proceedings of 2016 IEEE International Conference on Image Processing. Piscataway:IEEE Press, 2016: 2752-2756. |
[1] | Feibo JIANG, Yubo PENG, Li DONG. Deep image semantic communication model for 6G [J]. Journal on Communications, 2023, 44(3): 198-208. |
[2] | Julong LAN, Di ZHU, Dan LI. Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing [J]. Journal on Communications, 2022, 43(6): 143-155. |
[3] | Jie LAI, Xiaodan WANG, Qian XIANG, Yafei SONG, Wen QUAN. Review on autoencoder and its application [J]. Journal on Communications, 2021, 42(9): 218-230. |
[4] | Limin XIAO,Xiangrong XU,Zhuangkun WEI,Shenghan LIU,Yiwen LIU. Channel impulse response insensitive feature for non-coherent signal detection in molecular communication [J]. Journal on Communications, 2020, 41(9): 49-58. |
[5] | Chunxiang GU,Weisen WU,Ya’nan SHI,Guangsong LI. Method of unknown protocol classification based on autoencoder [J]. Journal on Communications, 2020, 41(6): 88-97. |
[6] | Jingyi QU,Meng YE,Xing QU. Airport delay prediction model based on regional residual and LSTM network [J]. Journal on Communications, 2019, 40(4): 149-159. |
[7] | Shan GAI. Feature extraction algorithm based on quaternion common spatial pattern for banknote recognition [J]. Journal on Communications, 2018, 39(12): 40-46. |
[8] | Wei-guo SHEN,Wei WANG. Keystroke features recognition based on stable linear discriminant analysis [J]. Journal on Communications, 2017, 38(Z2): 26-29. |
[9] | Xiao-lin XU,Xiao-chun YUN,Yong-lin ZHOU,Xue-bin KANG. Online analytical model of massive malware based on feature clusting [J]. Journal on Communications, 2013, 34(8): 146-153. |
[10] | . Online analytical model of massive malware based on feature clusting [J]. Journal on Communications, 2013, 34(8): 19-153. |
[11] | Ke QI,Da-fang ZHANG,Dong-qing XIE. Reliable steganalysis of color images based on color gradient sequence [J]. Journal on Communications, 2011, 32(1): 27-36. |
[12] | Run-qiang YAN,Yi-sheng ZHU. Speech endpoint detection based on recurrence rate analysis [J]. Journal on Communications, 2007, 28(1): 35-39. |
[13] | Xin-ran LIU. New network attack classification architecture [J]. Journal on Communications, 2006, 27(2): 160-167. |
[14] | Chun-yan QIU,Tie-li SUN. Design and implementation of a web-based feature extraction search engine [J]. Journal on Communications, 2005, 26(1A): 284-288. |
[15] | Yi LI,Qiu-qi RUAN. Texture classification by support vector machines [J]. Journal on Communications, 2005, 26(1): 114-119. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
|