Adversarial subdomain adaptation network for mismatched steganalysis

Lei ZHANG; Hongxia WANG

doi:10.11959/j.issn.2096-109x.2022028

您当前的位置：

首页 >

文章列表页 >

Adversarial subdomain adaptation network for mismatched steganalysis

Topic: Multimedia Content Security | 更新时间：2024-06-03

- Adversarial subdomain adaptation network for mismatched steganalysis
- Chinese Journal of Network and Information Security Vol. 8, Issue 3, Pages: 76-86(2022)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2022028
  CLC： TP309
- Online First：2022-06，
  
  Published：15 June 2022
- 稿件说明：
移动端阅览
Lei ZHANG, Hongxia WANG. Adversarial subdomain adaptation network for mismatched steganalysis[J]. Chinese Journal of Network and Information Security, 2022, 8(3): 76-86.
DOI：

Lei ZHANG, Hongxia WANG. Adversarial subdomain adaptation network for mismatched steganalysis[J]. Chinese Journal of Network and Information Security, 2022, 8(3): 76-86. DOI： 10.11959/j.issn.2096-109x.2022028.

摘要

当训练集数据和测试集数据来自不同的载体源时，即在载体源失配的条件下，通常会使一个表现优异的隐写分析器检测准确率下降。在实际应用中，隐写分析人员往往需要处理从互联网上采集的图像。然而，与训练集数据相比，这些可疑图像很可能具有完全不同的捕获和处理历史，导致隐写分析模型可能出现不同程度的检测性能下降，这也是隐写分析工具在现实应用中很难成功部署的原因。为了提高基于深度学习的隐写分析方法的实际应用价值，对测试样本信息加以利用，使用领域自适应方法来解决载体源失配问题，将训练集数据作为源领域，将测试集数据作为目标领域，通过最小化源领域与目标领域之间的特征分布差异来提高隐写分析器在目标领域的检测性能，提出了一种对抗子领域自适应网络（ASAN，adversarial subdomain adaptation network）。一方面从生成特征的角度出发，要求隐写分析模型生成的源领域特征和目标领域特征尽可能相似，使判别器分辨不出特征来自哪一个领域；另一方面从减小域间特征分布差异的角度出发，采用子领域自适应方法来减少相关子领域分布的非期望变化，有效地扩大了载体与载密样本之间的距离，有利于分类精度的提高。通过在多个数据集上对3种隐写算法进行检测，证实了所提方法可以有效地提升模型在数据集失配和算法失配时的检测准确率，减少了失配问题带给模型的负面影响。

Abstract

Once data in the training and test sets come from different cover sources

that is

under the condition of cover source mismatch

it usually makes the detection accuracy rate of an outstanding steganalysis model to be reduced.In practical applications

the analyzers need to process images collected from the Internet.However

compared with the training set data

these suspicious images are likely to have completely different capture and processing histories

which may lead to the degradation of steganalysis model.It is also why steganalysis tools are difficult to deploy successfully in the real-world applications.To improve the practical application value of steganalysis methods based on deep learning

test sample information is utilized and domain adaptation method is used to solve the problem of cover source mismatch.Regarding the training set data as the source domain and test set data as the target domain

the detection performance of steganalysis models in the target domain is enhanced by minimizing the discrepancy between the feature distribution of source domain and target domain.ASAN (adversarial subdomain adaptation network) was proposed from the perspective of feature generation on the one hand.The source domain features and target domain features generated by the steganalysis model were required to be as similar as possible

so that the discriminator cannot distinguish which domain the features came from.On the other hand

to reduce the difference of feature distribution between domains

the subdomain adaptation method was adopted to reduce the unexpected change of the distribution of related subdomains.The distance between the cover and stego samples was enlarged effectively to improve the classification accuracy.After testing three steganography algorithms on multiple datasets

it is confirmed that the proposed method can effectively improve the detection accuracy rate of the model in the case of dataset mismatch and algorithm mismatch and it can also reduce the negative impact of the mismatch problem of the model.

关键词

Keywords

references

HOLUB V , FRIDRICH J , DENEMARK T . Universal distortion function for steganography in an arbitrary domain [J ] . EURASIP Journal on Information Security , 2014 ,( 1 ): 1 - 13 .

HOLUB V , FRIDRICH J . Designing steganographic distortion using directional filters [C ] // Proceedings of 2012 IEEE International Workshop on Information Forensics and Security (WIFS) . 2012 : 234 - 239 .

PEVNÝ T , FILLER T , BAS P . Using high-dimensional image models to perform highly undetectable steganography [C ] // Proceedings of International Workshop on Information Hiding.Springer . 2010 : 161 - 177 .

FRIDRICH J , KODOVSKY J . Rich models for steganalysis of digital images [J ] . IEEE Transactions on Information Forensics and Security , 2012 , 7 ( 3 ): 868 - 882 .

SONG X , LIU F , YANG C , et al . Steganalysis of adaptive JPEG steganography using 2D Gabor filters [C ] // Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security . 2015 : 15 - 23 .

FENG G , ZHANG X , REN Y , et al . Diversity-based cascade filters for JPEG steganalysis [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2019 , 30 ( 2 ): 376 - 386 .

YE J , NI J , YI Y . Deep learning hierarchical representations for image steganalysis [J ] . IEEE Transactions on Information Forensics and Security , 2017 , 12 ( 11 ): 2545 - 2557 .

YEDROUDJ M , COMBY F , CHAUMONT M . Yedroudj-net:an efficient CNN for spatial steganalysis [C ] // 2018 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP) . 2018 : 2092 - 2096 .

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 .

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 , 2020 , 15 : 1138 - 1150 .

YOU W , ZHANG H , ZHAO X . A siamese CNN for image steganalysis [J ] . IEEE Transactions on Information Forensics and Security , 2021 , 16 : 291 - 306 .

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 .

JIA J , ZHAI L , REN W , et al . Transferable heterogeneous feature subspace learning for JPEG mismatched steganalysis [J ] . Pattern Recognition , 2020 , 100 : 107105 .

XUE Y , YANG L , WEN J , et al . A subspace learning-based method for JPEG mismatched steganalysis [J ] . Multimedia Tools and Applications , 2019 , 78 ( 7 ): 8151 - 8166 .

YANG Y , KONG X , FENG C . Double-compressed JPEG images steganalysis with transferring feature [J ] . Multimedia Tools and Applications , 2018 , 77 ( 14 ): 17993 - 18005 .

FENG C , KONG X W , LI M , et al . Contribution-based feature transfer for JPEG mismatched steganalysis [C ] // Proceedings of 2017 IEEE International Conference on Image Processing (ICIP) . 2017 : 500 - 504 .

YANG Y , KONG X , WANG B , et al . Steganalysis on Internet images via domain adaptive classifier [J ] . Neurocomputing , 2019 , 351 : 205 - 216 .

LONG M , WANG J , DING G , et al . Adaptation regularization:a general framework for transfer learning [J ] . IEEE Transactions on Knowledge and Data Engineering , 2013 , 26 ( 5 ): 1076 - 1089 .

ZHANG X , KONG X , WANG P , et al . Cover-source mismatch in deep spatial steganalysis [C ] // Proceedings of 18th Workshop on Digital Watermarking . 2019 : 71 - 83 .

HU D , MA Z , FAN Y , et al . Study on the interaction between the cover source mismatch and texture complexity in steganalysis [J ] . Multimedia Tools and Applications , 2019 , 78 ( 6 ): 7643 - 7666 .

张逸为 , 张卫明 , 俞能海 . 针对特定测试样本的隐写分析方法 [J ] . 软件学报 , 2018 , 29 ( 4 ): 987 - 1001 .

ZHANG Y W , ZHANG W M , YU N H . Specific testing sample steganalysis [J ] . Journal of Software , 2018 , 29 ( 4 ): 987 - 1001 .

OZCAN S , MUSTACOGLU A F . Transfer learning effects on image steganalysis with pre-trained deep residual neural network model [C ] // Proceedings of 2018 IEEE International Conference on Big Data (Big Data) . 2018 : 2280 - 2287 .

EL BEJI R , SAIDI M , HERMASSI H , et al . An improved CNN steganalysis architecture based on “Catalyst Kernels” and transfer learning [C ] // Proceedings of International Conference on Digital Economy . 2018 : 119 - 128 .

PEVNY T , FRIDRICH J . Merging Markov and DCT features for multi-class JPEG steganalysis [C ] // Proceedings of Security,Steganography,and Watermarking of Multimedia Contents IX . 2007 :650503.

HOLUB V , FRIDRICH J . Low-complexity features for JPEG steganalysis using undecimated DCT [J ] . IEEE Transactions on Information Forensics and Security , 2015 , 10 ( 2 ): 219 - 228 .

KODOVSKÝ J , FRIDRICH J , . Calibration revisited [C ] // Proceedings of the 11th ACM Workshop on Multimedia and Security . 2009 : 63 - 74 .

PAN S J , YANG Q . A survey on transfer learning [J ] . IEEE Transactions on Knowledge and Data Engineering , 2009 , 22 ( 10 ): 1345 - 1359 .

BORGWARDT K M , GRETTON A , RASCH M J , et al . Integrating structured biological data by kernel maximum mean discrepancy [J ] . Bioinformatics , 2006 , 22 ( 14 ): 49 - 57 .

TZENG E , HOFFMAN J , ZHANG N , et al . Deep domain confusion:Maximizing for domain invariance [J ] . arXiv preprint arXiv:1412.3474 , 2014 .

LONG M , CAO Y , WANG J , et al . Learning transferable features with deep adaptation networks [C ] // Proceedings of International Conference on Machine Learning . 2015 : 97 - 105 .

LONG M , ZHU H , WANG J , et al . Deep transfer learning with joint adaptation networks [C ] // Proceedings of International Conference on Machine Learning . 2017 : 2208 - 2217 .

ZHU Y , ZHUANG F , WANG J , et al . Deep subdomain adaptation network for image classification [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2020 , 32 ( 4 ): 1713 - 1722 .

GANIN Y , USTINOVA E , AJAKAN H , et al . Domain-adversarial training of neural networks [J ] . The Journal of Machine Learning Research , 2016 , 17 ( 1 ): 2096 - 2030 .

YU C , WANG J , CHEN Y , et al . Transfer learning with dynamic adversarial adaptation network [C ] // Proceedings of 2019 IEEE International Conference on Data Mining (ICDM) . 2019 : 778 - 786 .

BAS P , FILLER T , PEVNÝ T , . “Break our steganographic system”:the ins and outs of organizing BOSS [C ] // Proceedings of International Workshop on Information Hiding . 2011 : 59 - 70 .

SCHAEFER G , STICH M . UCID:an uncompressed color image database [C ] // Proceedings of Storage and Retrieval Methods and Applications for Multimedia . 2004 : 472 - 480 .

AGUSTSSON E , TIMOFTE R . Ntire 2017 challenge on single image super-resolution:Dataset and study [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops . 2017 : 126 - 135 .

HUISKES M J , LEW M S . The MIR flickr retrieval evaluation [C ] // Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval . 2008 : 39 - 43 .

VAN DER MAATEN L , HINTON G . Visualizing data using t-SNE [J ] . Journal of Machine Learning Research , 2008 , 9 ( 11 ): 2579 - 2605 .

Views

205

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰