Image steganalysis method based on saliency detection

doi:10.11772/j.issn.1001-9081.2020081323

Abstract

Abstract: Aiming at the problem that the steganalysis of images is difficult, and the existing detection models are difficult to make a targeted analysis of steganography regions of images, a method for image steganalysis based on saliency detection was proposed. In the proposed method, the saliency detection was used to guide the steganalysis model to focus on the image features of steganography regions. Firstly, the saliency detection module was used to generate saliency regions of the image. Secondly, the region filter module was used to filter the saliency images with a high degree of coincidence with the steganography regions, and image fusion technology was used to fuse them with the original images. Finally, the quality of training set was improved by replacing the error detection images with their corresponding saliency fusion images, so as to improve the training effect and detection ability of the model. The experiments were carried out on BOSSbase1.01 dataset. The dataset was embedded by image adaptive steganography algorithms in spatial domain and JPEG domain respectively, and experimental results show that the proposed method can effectively improve the the detection accuracy for deep learning-based steganalysis model by 3 percentage points at most. The mismatch test was also carried out on IStego100K dataset to further verify the generalization ability of the model and improve its application value. According to the result of the mismatch test, the proposed method has certain generalization ability.

Key words: adaptive image steganography, image steganalysis, saliency detection, image fusion, deep learning

摘要： 针对图像隐写分析难度大、现有的检测模型难以对图像隐写区域进行针对性检测的问题，提出了一种基于显著性检测的图像隐写分析方法。该方法利用显著性检测技术引导隐写分析模型更加关注图像隐写区域的特征。首先，显著性检测模块生成图像的显著性区域；其次，区域筛选模块筛选出与隐写区域重合度较高的显著性图，利用图像融合技术与原始图像进行融合；最后，用相应的显著性融合图替换检测错误的图像，提高训练集质量，从而提升模型的训练效果和检测能力。实验使用BOSSbase1.01数据集，分别用空域和JPEG域的自适应隐写算法对数据集嵌入后进行隐写分析，结果表明，该方法能够有效提升现有基于深度学习的隐写分析模型的检测准确率，最多可提升3个百分点。为了进一步验证该方法的泛化性和提高其实用价值，在IStego100K数据集上进行了模型失配测试，测试结果也表明该方法具有一定的泛化能力。

关键词: 图像自适应隐写术, 图像隐写分析, 显著性检测, 图像融合, 深度学习

CLC Number:

TP309.2

HUANG Siyuan, ZHANG Minqing, KE Yan, BI Xinliang. Image steganalysis method based on saliency detection[J]. Journal of Computer Applications, 2021, 41(2): 441-448.

黄思远, 张敏情, 柯彦, 毕新亮. 基于显著性检测的图像隐写分析方法[J]. 计算机应用, 2021, 41(2): 441-448.

References

[1] 柯彦,张敏情,刘佳. 可分离的加密域十六进制可逆信息隐藏[J]. 计算机应用,2016,36(11):3082-3087.(KE Y,ZHANG M Q, LIU J. Separable reversible hexadecimal data hiding in encrypted domain[J]. Journal of Computer Applications,2016,36(11):3082-3087.)
[2] 任魏翔,翟黎明,王丽娜,等. 基于卷积神经网络的JPEG图像隐写分析参照图像生成方法[J]. 计算机研究与发展,2019,56(10):2250-2261.(REN W X,ZHAI L M,WANG L N,et al. Reference image generation algorithm for JPEG image steganalysis based on convolutional neural network[J]. Journal of Computer Research and Development,2019,56(10):2250-2261.)
[3] FRIDRICH J,KODOVSKY J. 1 Rich models for steganalysis of digital images[J]. IEEE Transactions on Information Forensics and Security,2012,7(3):868-882.
[4] QIAN Y,DONG J,WANG W,et al. Deep learning for steganalysis via convolutional neural networks[C]//Proceedings of the 2015 International Society for Optical Engineering. San Francisco:SPIE,2015:9409:94090J-1-94090J-10.
[5] XU G,WU H-Z,SHI Y-Q. Structural design of convolutional neural networks for steganalysis[J]. IEEE Signal Processing Letters,2016,23(5):708-712.
[6] 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.
[7] BOROUMAND M,CHEN M,FRIDRICH J. Deep residual network for steganalysis of digital images[J]. IEEE Transactions on Information Forensics and Security,2018,14(5):1181-1193.
[8] ZHANG R, ZHU F, LIU J, 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,2019,15:1138-1150.
[9] 张敏情,黄思远,柯彦, 等. 一种基于显著性检测的图像自适应隐写分析系统及方法:202010524234.1[P/OL]. 2020-06-10[2020-12-10]. http://cprs.patentstar.com.cn/Search/Detail?ANE=6BDA7FCA9CHC9FHD9DID9IDC7CAA9BIC8AEA6DBA7CCA9HAC.(ZHANG M Q,HUANG S Y,KE Y,et al. Image adaptive steganalysis system and method based on saliency detection:202010524234.1[P/OL]. 2020-06-10[2020-12-10]. http://cprs.patentstar.com.cn/Search/Detail?ANE=6BDA7FCA9CHC9FHD9DID9IDC7CAA9BIC8AEA6DBA7CCA9HAC.)
[10] PEVNY 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 6958. Berlin:Springer,2010:161-177.
[11] HOLUB V,FRIDRICH J. Designing steganographic distortion using directional filters[C]//Proceedings of the 2013 IEEE International Workshop on Information Forensic and Security. Piscataway:IEEE,2013:234-239.
[12] HOLUB V,FRIDRICH J,DENEMARK T. Universal distortion function for steganography in an arbitrary domain[J]. EURASIP Journal on Information Security,2014,1(1):1.
[13] GUO L,NI J,SHI Y Q. An efficient JPEG steganographic scheme using uniform embedding[C]//2012 IEEE International Workshop on Information Forensics and Security. Piscataway:IEEE,2012:2-5.
[14] BORJI A,ITTI L. State-of-the-art in visual attention modeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(1):185-207.
[15] ZUND F,PRITCH Y,SORKINE-HORNUNG A,et al. Contentaware compression using saliency-driven image retargeting[C]//Proceedings of the 2013 IEEE International Conference on Image Processing. Piscataway:IEEE,2013:1845-1849.
[16] SHUI L L. Aptive image segmentation based on saliency detection[J]. International Journal on Smart Sensing and Intelligent Systems,2015,8(1):408-428.
[17] ZHANG F,DU B,ZHANG L. Saliency-guided unsupervised feature learning for scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2015,53(4):2175-2184.
[18] ITTI L,KOCH C,NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(11):1254-1259.
[19] CHENG M-M,MITRA N J,HUANG X,et al. Global contrast based salient region detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):569-582.
[20] ZHANG L,DAI J,LU H,et al. A bi-directional message passing model for salient object detection[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2018:1741-1750.
[21] CHEN S,TAN X,WANG B,et al. Reverse attention for salient object detection[C]//Proceedings of the 2018 European Conference on Computer Vision,LNCS 11213. Cham:Springer, 2018:234-250.
[22] LIU N,HAN J W,YANG M. Picanet:Learning pixel-wise contextual attention for saliency detection[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2018:3089-3098.
[23] QIN X,ZHANG Z,HUANG C,et al. BASNET:Boundary-aware salient object detection[C]//Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2019:7479-7489.
[24] LU J,ZHOU G,YANG C,et al. Steganalysis of content-adaptive steganography based on massive datasets pre-classification and feature selection[J]. IEEE Access,2019,7:21702-21711.
[25] RONNEBERGER O, FISCHER P, BROX T. U-Net:convolutional networks for biomedical image segmentation[C]//Proceedings of the 2015 International Conference on Medical Image Computing and Computer-assisted Intervention, LNCS 9351. Cham:Springer,2015:234-241.
[26] YAN Q,XU L,SHI J,et al. Hierarchical saliency detection[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2013:1155-1162.
[27] BAS P, FILLER T, PEVNY T. "Break our steganographic system":The ins and outs of organizing BOSS[C]//Proceedings of the 2011 International Workshop on Information Hiding,LNCS 6958. Berlin:Springer,2011:59-70.
[28] GUO L,NI J,SHI Y Q. Uniform embedding for efficient JPEG steganography[J]. IEEE Transactions on Information Forensics and Security,2014,9(5):814-825.
[29] LI B,WANG M,HUANG J,et al. A new cost function for spatial image steganography[C]//Proceedings of the 2014 IEEE International Conference on Image Processing. Piscataway:IEEE, 2014:4206-4210.
[30] KINGMA D P,BA J. Adam:a method for stochastic optimization[EB/OL]. (2017-01-30)[2020-07-25]. http://arxiv.org/abs/1412.6980
[31] XU G. Deep convolutional neural network to detect J-UNIWARD[C]//Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security. New York:ACM,2017:67-73.
[32] CHEN M,SEDIGHI V,BOROUMAND M,et al. JPEG-phaseaware convolutional neural network for steganalysis of JPEG images[C]//Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security. New York:ACM, 2017:67-73.
[33] DENEMARK T,BOROUMAND M,FRIDRICH J. Steganalysis features for content adaptive JPEG steganography[J]. IEEE Transactions on Information Forensics and Security,2016,11(8):1736-1746.
[34] FRIDRICH J, KODOVSKÝ J. Rich models for steganalysis ofdigital images[J]. IEEE Transactions on Information Forensics and Security,2012,7(3):868-882.
[35] YANG Z,WANG K,MA S,et al. IStego100K:large-scale image steganalysis dataset[EB/OL]. (2019-11-13)[2020-07-20]. https://arxiv.org/pdf/1911.05542.pdf.