Skip to content
2000
  1. Home
  2. A-Z Publications
  3. International Journal of Sensors Wireless Communications and Control
  4. Volume 14, Issue 4
  5. Article
Volume 14, Issue 4
  • ISSN: 2210-3279
  • E-ISSN: 2210-3287

Abstract

Steganography is the technique of hiding data for secret communication in a public media format. The image in which the hidden data is stored is called a stego image. Steganalysis is the process of targeting the methods of steganography to identify, remove, destroy, and exploit the secret data in stego images. The identification of embedded secret data in the image is the basis for steganalysis. The proper selection of the type and composition of cover files contributes to a better embedding. Several steganalysis techniques exist for detecting steganography in the images given. Because of the embedded data, the performance of the steganalysis technique relies on the capacity to retrieve the feature representations to identify the statistical portion of the image. Steganalysis & steganography has experienced tremendous development in recent years with the emergence of Deep Convolution Neural Networks (DCNN). In this paper, we explored the current state of research from the latest systems of image steganalysis based on deep learning. This paper presents different methodologies and frameworks of CNN, the research being carried out on image steganalysis based on deep learning and implementation complexities, and highlights the benefits and limitations of the existing techniques. This study also provides the direction for future research and may serve as a fundamental source for further research in deep learning-based image steganalysis.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/swcc/10.2174/0122103279296370240529075507
2024-06-24
2024-11-22

  • From This Site

    /content/journals/swcc/10.2174/0122103279296370240529075507
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. HussainI. ZengJ. A survey on deep convolutional neural networks for image steganography and steganalysis.Trans. Internet Inf. Syst. (Seoul)20201431228124810.3837/tiis.2020.03.017
     [Google Scholar]
  2. JinaChanu Y, Manglem Singh K, Tuithung T. Image steganography and steganalysis: A survey.Int. J. Comput. Appl.201252211110.5120/8171‑1484
     [Google Scholar]
  3. PevnýT. FillerT. BasP. Using high-dimensional image models to perform highly undetectable steganography. Information Hiding.Berlin, HeidelbergSpringer Link201010.1007/978‑3‑642‑16435‑4_13
     [Google Scholar]
  4. LiB. WangM. HuangJ. LiX. A new cost function for spatial image steganography.2014 IEEE Int Conf Image Process ICIP 201429 January 2015Paris, France201442061010.1109/ICIP.2014.7025854
     [Google Scholar]
  5. HolubV. FridrichJ. Designing steganographic distortion using directional filters.2012 IEEE International Workshop on Information Forensics and Security (WIFS)02-05 December 2012Costa Adeje, Spain201210.1109/WIFS.2012.6412655
     [Google Scholar]
  6. HolubV. FridrichJ. DenemarkT. Universal distortion function for steganography in an arbitrary domain.EURASIP J. Multimed. Inf. Secur.20142014111310.1186/1687‑417X‑2014‑1
     [Google Scholar]
  7. JinZ. YangY. ChenY. ChenY. IAS-CNN: Image adaptive steganalysis via convolutional neural network combined with selection channel.Int. J. Distrib. Sens. Netw.2020163210.1177/1550147720911002
     [Google Scholar]
  8. WestfeldA. F5-A steganographic algorithm high capacity despite better steganalysis.Information Hiding2001
     [Google Scholar]
  9. PanY. NiJ. SuW. Improved uniform embedding for efficient JPEG steganography. Cloud Computing And Security.Berlin, HeidelbergSpringer Link201610.1007/978‑3‑319‑48671‑0_12
     [Google Scholar]
  10. GuoL. NiJ. SuW. TangC. ShiY.Q. Using statistical image model for JPEG Steganography: Uniform embedding Revisited.IEEE Trans. Inf. Forensics Security201510122669268010.1109/TIFS.2015.2473815
     [Google Scholar]
  11. ReinelT-S RaúlRP GustavoI Deep learning applied to steganalysis of digital images: A systematic review.IEEE Access20197689709010.1109/ACCESS.2019.2918086
     [Google Scholar]
  12. LiuF YanX LuY Feature selection for image steganalysis using binary bat algorithm.IEEE Access202084244910.1109/ACCESS.2019.2963084
     [Google Scholar]
  13. L R, B L. Approaches and methods for Steganalysis – A survey.Int. J. Adv. Res. Comput. Commun. Eng.20176643343810.17148/IJARCCE.2017.6678
     [Google Scholar]
  14. KimD.H. LeeH.Y. Deep learning-based steganalysis against spatial domain steganography.2017 European Conference on Electrical Engineering and Computer Science (EECS)17-19 November 2017Bern, Switzerland20181410.1109/EECS.2017.9
     [Google Scholar]
  15. FridrichJ. KodovskyJ. Rich models for steganalysis of digital images.IEEE Trans. Inf. Forensics Security20127386888210.1109/TIFS.2012.2190402
     [Google Scholar]
  16. KodovskýJ. FridrichJ. HolubV. Ensemble classifiers for steganalysis of digital media.IEEE Trans. Inf. Forensics Security20127243244410.1109/TIFS.2011.2175919
     [Google Scholar]
  17. ChangC.C. LinC.J. LIBSVM: A library for support vector machines.ACM Trans. Intell. Syst. Technol.20112312710.1145/1961189.1961199
     [Google Scholar]
  18. OzcanS. MustacogluA.F. Transfer learning effects on image steganalysis with pre-trained deep residual neural network model.Big Data201820182280228710.1109/BigData.2018.8622437
     [Google Scholar]
  19. QianY. DongJ. WangW. TanT. Deep learning for steganalysis via convolutional neural networks.Proceedings of SPIE - The International Society for Optical Engineering 9409.San Francisco, California, United States20151010.1117/12.2083479
     [Google Scholar]
  20. PoultneyC. ChopraS. CunY.L. Efficient learning of sparse representations with an energy-based model.Adv. Neural Inf. Process. Syst.200611371144
     [Google Scholar]
  21. BengioY. LamblinP. PopoviciD. LarochelleH. Greedy layer-wise training of deep networks.Adv. Neural Inf. Process. Syst.20071153160
     [Google Scholar]
  22. LeCunY. BengioY. HintonG. Deep learning.Nature2015521755343644410.1038/nature14539 26017442
     [Google Scholar]
  23. YangP. BaracchiD. NiR. ZhaoY. ArgentiF. PivaA. A survey of deep learning-based source image forensics.J. Imaging202063910.3390/jimaging6030009 34460606
     [Google Scholar]
  24. SzegedyC. Going deeper with convolutions.2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)07-12 June 2015; Boston, MA, USA20151910.1109/CVPR.2015.7298594
     [Google Scholar]
  25. SimonyanK. ZissermanA. Very deep convolutional networks for large-scale image recognition.arXiv:14091556 2015
     [Google Scholar]
  26. HeK. ZhangX. RenS. SunJ. Deep residual learning for image recognition.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)27-30 June 2016Las Vegas, NV, USA2016770810.1109/CVPR.2016.90
     [Google Scholar]
  27. WuS. ZhongS. LiuY. Deep residual learning for image steganalysis. Multimedia Tools and Applications.Berlin, HeidelbergSpringer Link201710.1007/s11042‑017‑4440‑4
     [Google Scholar]
  28. KhanA. SohailA. ZahooraU. QureshiA.S. A survey of the recent architectures of deep convolutional neural networks.Artif. Intell. Rev.20205385455551610.1007/s10462‑020‑09825‑6
     [Google Scholar]
  29. PhungV.H. RheeE.J. A High-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets.Appl. Sci. (Basel)2019921450010.3390/app9214500
     [Google Scholar]
  30. KrizhevskyA. SutskeverI. HintonG.E. ImageNet classification with deep convolutional neural networks.Commun. ACM2017606849010.1145/3065386
     [Google Scholar]
  31. HuangG. LiuZ. Van Der MaatenL. WeinbergerK.Q. Densely connected convolutional networks.2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)21-26 July 2017Honolulu, HI, USA201710.1109/CVPR.2017.243
     [Google Scholar]
  32. SzegedyC. VanhouckeV. IoffeS. ShlensJ. WojnaZ. Rethinking the inception architecture for computer vision.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)12 December 2016Las Vegas, NV, USA20162818282610.1109/CVPR.2016.308
     [Google Scholar]
  33. SzegedyC. IoffeS. VanhouckeV. AlemiA.A. Inception-v4, inception- ResNet and the impact of residual connections on learning 31st AAAI Conf Artif Intell AAAI 2017; 201731142788410.1609/aaai.v31i1.11231
     [Google Scholar]
  34. ZeilerM.D. FergusR. Visualizing and understanding convolutional networks. Computer Vision – ECCV 2014.Berlin, HeidelbergSpringerLink201410.1007/978‑3‑319‑10590‑1_53
     [Google Scholar]
  35. TianJ. LiY. Convolutional neural networks for steganalysis via transfer learning.Int. J. Pattern Recognit. Artif. Intell.2019332195900610.1142/S0218001419590067
     [Google Scholar]
  36. KaurR. KaurB. Licensed under creative commons attribution CC BY A study and review of techniques of spatial steganography.Int. J. Sci. Res.20134431983203
     [Google Scholar]
  37. TanS. LiB. Stacked convolutional auto-encoders for steganalysis of digital images. Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific. 16 February 2015;Siem Reap, Cambodia.201410.1109/APSIPA.2014.7041565
     [Google Scholar]
  38. PevnyT. Steganalysis by subtractive pixel adjacency matrix.IEEE Trans. Inf. Forensics Security20095215224
     [Google Scholar]
  39. QianY. DongJ. WangW. TanT. Learning and transferring representations for image steganalysis using convolutional neural network.2016 IEEE International Conference on Image Processing (ICIP)19 August 2016Phoenix, AZ, USA201610.1109/ICIP.2016.7532860
     [Google Scholar]
  40. XuG. WuH.Z. ShiY.Q. Structural design of convolutional neural networks for steganalysis.IEEE Signal Process. Lett.201623570871210.1109/LSP.2016.2548421
     [Google Scholar]
  41. IoffeS. SzegedyC. Batch normalization: Accelerating deep network training by reducing internal covariate shift.arXiv:1502031672015
     [Google Scholar]
  42. XuG. WuH.Z. ShiY.Q. Ensemble of CNNs for steganalysis : An empirical study. Information Hiding and Multimedia Security.New York, NY, United StatesAssociation for Computing Machinery201610310710.1145/2909827.2930798
     [Google Scholar]
  43. PibreL. PasquetJ. IencoD. ChaumontM. Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch.arXiv:151104855201610.2352/ISSN.2470‑1173.2016.8.MWSF‑078
     [Google Scholar]
  44. CouchotJ-F. CouturierR. GuyeuxC. SalomonM. Steganalysis via a convolutional neural network using large convolution filters for embedding process with same stego key.arXiv:1605079462016
     [Google Scholar]
  45. SedighiV. FridrichJ. Histogram layer, moving convolutional neural networks towards feature-based steganalysis.Electron Imaging201720177505510.2352/ISSN.2470‑1173.2017.7.MWSF‑325
     [Google Scholar]
  46. HolubV. FridrichJ. Random projections of residuals for digital image steganalysis.IEEE Trans. Inf. Forensics Security20138121996200610.1109/TIFS.2013.2286682
     [Google Scholar]
  47. YeJ. NiJ. YiY. Deep Learning Hierarchical Representations for Image Steganalysis.IEEE Trans. Inf. Forensics Security201712112545255710.1109/TIFS.2017.2710946
     [Google Scholar]
  48. WuS. ZhongS. LiuY. A novel convolutional neural network for image steganalysis with shared normalization.IEEE Trans. Multimed.202022125627010.1109/TMM.2019.2920605
     [Google Scholar]
  49. LiB. WeiW. FerreiraA. TanS. ReST-Net: Diverse activation modules and parallel subnets-based CNN for spatial image steganalysis.IEEE Signal Process. Lett.201825565065410.1109/LSP.2018.2816569
     [Google Scholar]
  50. YedroudjM. CombyF. ChaumontM. Yedroudj-Net: An efficient CNN for spatial steganalysis.arXiv:180300407201810.1109/ICASSP.2018.8461438
     [Google Scholar]
  51. ZhangR. ZhuF. LiuJ. LiuG. Efficient feature learning and multisize image steganalysis based on CNN.arXiv:1807114282018
     [Google Scholar]
  52. DengpanY. ShunzhiJ. ShiyuL. Faster and transferable deep learning steganalysis on GPU.J. Real-Time Image Process.20191662363310.1007/s11554‑019‑00870‑1
     [Google Scholar]
  53. KatoH OsugeK HarutaS SasaseI. A preprocessing by using multiple steganography for intentional image downsampling on CNN-based steganalysis.IEEE Access202081955789310.1109/ACCESS.2020.3033814
     [Google Scholar]
  54. NtivuguruzwaJ.D.L.C. AhmadT. A convolutional neural network to detect possible hidden data in spatial domain images.Cyber Secur2023612310.1186/s42400‑023‑00156‑x
     [Google Scholar]
  55. ReinelTS BrayanA-AH AlejandroB-OM GBRAS-Net: A convolutional neural network architecture for spatial image steganalysis.IEEE Access20219January143405010.1109/ACCESS.2021.3052494
     [Google Scholar]
  56. DuanX. ZhangC. MaY. LiuS. Preprocessing enhancement method for spatial domain steganalysis.Mathematics20221021393610.3390/math10213936
     [Google Scholar]
  57. LiuS. ZhangC. WangL. YangP. HuaS. ZhangT. Image steganalysis of low embedding rate based on the attention mechanism and transfer learning.Electronics (Basel)202312496910.3390/electronics12040969
     [Google Scholar]
  58. Bravo-OrtizM.A. Mercado-RuizE. Villa-PulgarinJ.P. CVTStego-Net: A convolutional vision transformer architecture for spatial image steganalysis.J Inf Secur Appl202481January10369510.1016/j.jisa.2023.103695
     [Google Scholar]
  59. XuG. Deep convolutional neural network to detect J-UNIWARD. Information Hiding and Multimedia Security.New York, NY, United StatesAssociation for Computing Machinery2017677310.1145/3082031.3083236
     [Google Scholar]
  60. ZengJ. TanS. LiB. HuangJ. Large-scale JPEG Image Steganalysis using hybrid deep-learning framework.IEEE Trans. Inf. Forensics Security20181351200121410.1109/TIFS.2017.2779446
     [Google Scholar]
  61. ZhongK. FengG. ShenL. LuoJ. Deep learning for steganalysis based on filter diversity selection.Sci. China Inf. Sci.2018611212910510.1007/s11432‑018‑9640‑7
     [Google Scholar]
  62. ChenM. SedighiV. BoroumandM. FridrichJ. JPEG-phase-aware convolutional neural network for steganalysis of JPEG images. Information Hiding and Multimedia Security.New York, NY, United StatesAssociation for Computing Machinery2017758410.1145/3082031.3083248
     [Google Scholar]
  63. YangJ. ShiY-Q. WongE.K. KangX. JPEG Steganalysis based on DenseNet.arXiv:1711093352017
     [Google Scholar]
  64. HuD ZhouS ShenQ ZhengS ZhaoZ FanY. Digital Image Steganalysis based on visual attention and deep reinforcement learning. IEEE Access 20197c259243510.1109/ACCESS.2019.2900076
     [Google Scholar]
  65. DengX.Q. ChenB.L. LuoW.Q. LuoD. Universal Image steganalysis based on convolutional neural network with global covariance pooling.J. Comput. Sci. Technol.20223751134114510.1007/s11390‑021‑0572‑0
     [Google Scholar]
  66. LiuQ. YangZ. WuH. JPEG Steganalysis based on Steganographic feature enhancement and graph attention learning.J. Electron. Imaging202332311010.1117/1.JEI.32.3.033032
     [Google Scholar]
  67. MoC. LiuF. ZhuM. YanG. QiB. YangC. Image Steganalysis based on deep content features clustering.Comput. Mater. Continua20237632921293610.32604/cmc.2023.039540
     [Google Scholar]
  68. ZhangJ. ZhaoX. HeX. Robust JPEG steganography based on the robustness classifier.EURASIP J. Inf. Secur.2023202311110.1186/s13635‑023‑00148‑x
     [Google Scholar]
  69. YangJ. KangX. WongE.K. ShiY.Q. Deep learning with feature reuse for JPEG image steganalysis.2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).10.23919/APSIPA.2018.8659589
     [Google Scholar]
  70. QianY. DongJ. WangW. TanT. Feature learning for steganalysis using convolutional neural networks.Multimedia Tools Appl.20187715196331965710.1007/s11042‑017‑5326‑1
     [Google Scholar]
  71. LuY.Y. YangZ.L.O. ZhengL. ZhangY. Importance of truncation activation in pre-processing for spatial and Jpeg image steganalysis.2019 IEEE International Conference on Image Processing (ICIP)22-25 September 2019Taipei, Taiwan201910.1109/ICIP.2019.8803800
     [Google Scholar]
  72. ZengJ. TanS. LiuG. LiB. HuangJ. WISERNet: Wider separate-then-reunion network for steganalysis of color images.IEEE Trans. Inf. Forensics Security201914102735274810.1109/TIFS.2019.2904413
     [Google Scholar]
  73. ZhangT. ZhangH. WangR. WuY. A new JPEG image steganalysis technique combining rich model features and convolutional neural networks.Math. Biosci. Eng.20191654069408110.3934/mbe.2019201 31499650
     [Google Scholar]
  74. BoroumandM. ChenM. FridrichJ. Deep residual network for steganalysis of digital images.IEEE Trans. Inf. Forensics Security20191451181119310.1109/TIFS.2018.2871749
     [Google Scholar]
  75. Tabares-SotoR. Arteaga-ArteagaH.B. Mora-RubioA. Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain.PeerJ Comput. Sci.20217e45110.7717/peerj‑cs.451 33954236
     [Google Scholar]
  76. ShankarD.D. AzhakathA.S. Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization.Sci. Rep.2023131235910.1038/s41598‑023‑29453‑8 36759703
     [Google Scholar]
  77. OzcanS. MustacogluA.F. Transfer Learning effects on image steganalysis with pre-trained deep residual neural network model.2018 IEEE International Conference on Big Data (Big Data).10-13 December 2018; Seattle, WA, USA. 201810.1109/BigData.2018.8622437
     [Google Scholar]
  78. ShankaraD.D. UpadhyayP.K. Blind steganalysis for JPEG images using SVM and SVM-PSO classifiers.Int. J. Innov. Technol. Explor. Eng.2019811S1810.35940/ijitee.K1250.09811S19
     [Google Scholar]
  79. KishoreA. Deep learning approaches to universal and practical steganalysis2020
     [Google Scholar]
/content/journals/swcc/10.2174/0122103279296370240529075507
Loading
Image Steganalysis using Deep Convolution Neural Networks: A Literature Survey
Int J Sensors Wirel Commun Control 14, 247 (2024); https://doi.org/10.2174/0122103279296370240529075507
/content/journals/swcc/10.2174/0122103279296370240529075507
/content/journals/swcc/10.2174/0122103279296370240529075507
Loading

Data & Media loading...


  • Article Type:     Review Article
Keyword(s): Convolution Neural Network (CNN); cybersecurity; deep learning; embedded data; steganalysis; Steganography

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test