Skip to main content
Log in

Steganography and Steganalysis (in digital forensics): a Cybersecurity guide

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Steganography and steganalysis is a relatively new-fangled scientific discipline in security systems and digital forensics, respectively, but one that has matured greatly over the past two decades. In any specialism of human endeavour, it is imperative to periodically pause and review the state of the discipline for what has been achieved till date. This article scrutinizes where the discipline of steganography and steganalysis at this point in time in context to the common user and new researchers in terms of current trends. Also, what has been accomplished in order to critically examine what has been done well and what ought to be done better. The state-of-the-art techniques for steganography and steganalysis (image and video) have been deliberated for the last 5 years literature. Further, the paper also takes stock the dataset and tools available for multimedia steganography and steganalysis with the examples where steganography has been used in real-life. It is a corpus of the author’s opinion and the viewpoints of different other researchers and practitioners, working in this discipline. Additionally, experiments were done using image steganography techniques to analyse the recent trends. This survey is intended to provide a complete guide for common people and new researchers and scholars approaching this field, sight on the existing and the future of steganography and steganalysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. http://news.bbc.co.uk/2/hi/science/nature/2082657.stm

  2. https://www.darkreading.com/risk/research-shows-image-based-threat-on-the-rise/d/d-id/1129071?

  3. https://www.peerlyst.com/posts/using-digital-steganography-to-protect-national-security-information-ian-barwise-m-s-cissp-ceh-cnda

  4. https://www.dnaindia.com/mumbai/report-mumbai-police-fail-to-crack-july-11-suspects-mail-1058716

References

  1. Aach T, Kaup A, Mester R (1993) Statistical model-based change detection in moving video. Signal Process 31(2):165–180

    MATH  Google Scholar 

  2. M Abdolmohammadi, RM Toroghi, and A Bastanfard (2019). “Video Steganography Using 3D Convolutional Neural Networks,” in Mediterranean Conference on Pattern Recognition and Artificial Intelligence, pp. 149–161

  3. S Abu-El-Haija, N Kothari, J Lee, P Natsev, G Toderici, B Varadarajan, and S Vijayanarasimhan (2016). “Youtube-8m: A large-scale video classification benchmark,” arXiv Prepr. arXiv1609.08675

  4. S Alam, T Ahmad, and MN Doja (2017). “A Novel Edge Based Chaotic Steganography Method Using Neural Network,” in Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications, pp. 467–475

  5. Al-Dmour H, Al-Ani A (2016) A steganography embedding method based on edge identification and XOR coding. Expert Syst Appl 46:293–306

    Google Scholar 

  6. Aly HA (2011) Data hiding in motion vectors of compressed video based on their associated prediction error. IEEE Trans Inf Forensics Secur 6(1):14–18

    Google Scholar 

  7. Asikuzzaman M, Pickering MR (2018) An overview of digital video watermarking. IEEE Trans Circuits Syst Video Technol 28(9):2131–2153

    Google Scholar 

  8. Atawneh S, Almomani A, Al Bazar H, Sumari P, Gupta B (2017) Secure and imperceptible digital image steganographic algorithm based on diamond encoding in DWT domain. Multimed Tools Appl 76(18):18451–18472

    Google Scholar 

  9. Attaby AA, Ahmed MFMM, Alsammak AK (2018) Data hiding inside JPEG images with high resistance to steganalysis using a novel technique: DCT-M3. Ain Shams Eng J 9(4):1965–1974

    Google Scholar 

  10. Babaguchi N, Cavallaro A, Chellappa R, Dufaux F, Wang L (2013) Guest editorial: special issue on intelligent video surveillance for public security and personal privacy. IEEE Trans Inf Forensics Secur 8(10):1559–1561

    Google Scholar 

  11. RJ Bagnall (2002). “Reversing the steganography myth in terrorist operations: The asymmetrical threat of simple intelligence dissemination techniques using common tools,” SANS Inf. Secur. Read. Room, vol. 19

  12. Balasubramanian C, Selvakumar S, Geetha S (2014) High payload image steganography with reduced distortion using octonary pixel pairing scheme. Multimed Tools Appl 73(3):2223–2245

    Google Scholar 

  13. Balu S, Babu CNK, Amudha K (2018) Secure and efficient data transmission by video steganography in medical imaging system. Clust Comput:1–7

  14. FC Bancroft and C Clelland (2001). “DNA-based steganography.” Google Patents

  15. BG Banik and SK Bandyopadhyay (2017). “Image Steganography using BitPlane complexity segmentation and hessenberg QR method,” in Proceedings of the First International Conference on Intelligent Computing and Communication, pp. 623–633

  16. M Barni, G Cancelli, and A Esposito (2010). “Forensics aided steganalysis of heterogeneous images,” in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1690–1693

  17. N Beebe (2009). “Digital forensic research: The good, the bad and the unaddressed,” in IFIP International Conference on Digital Forensics, pp. 17–36

  18. Cao Y, Zhang H, Zhao X, Yu H (2015) Covert communication by compressed videos exploiting the uncertainty of motion estimation. IEEE Commun Lett 19(2):203–206

    Google Scholar 

  19. Y Cao, X Zhao, D Feng, and R Sheng (2011). “Video steganography with perturbed motion estimation,” in International Workshop on Information Hiding, pp. 193–207

  20. G Cao, Y Zhao, and R Ni (2008). “Image composition detection using object-based color consistency,” in 2008 9Th international conference on signal processing, pp. 1186–1189

  21. Chaeikar SS, Zamani M, Manaf ABA, Zeki AM (2018) PSW statistical LSB image steganalysis. Multimed Tools Appl 77(1):805–835

    Google Scholar 

  22. Chakraborty S, Jalal AS (2020) A novel local binary pattern based blind feature image steganography. Multimed Tools Appl:1–14

  23. Chakraborty S, Jalal AS, Bhatnagar C (2017) LSB based non blind predictive edge adaptive image steganography. Multimed Tools Appl 76(6):7973–7987

    Google Scholar 

  24. Chan C-K, Cheng L-M (2004) Hiding data in images by simple LSB substitution. Pattern Recogn 37(3):469–474

    MATH  Google Scholar 

  25. Cheddad A, Condell J, Curran K, Mc Kevitt P (2010) Digital image steganography: survey and analysis of current methods. Signal Process 90(3):727–752

    MATH  Google Scholar 

  26. Chen M, Boroumand M, Fridrich J (2018) Deep learning regressors for quantitative steganalysis. Electron Imaging 2018(7):160–161

    Google Scholar 

  27. Chen S, Qu Z (2018) Novel quantum video steganography and authentication protocol with large payload. Int J Theor Phys 57(12):3689–3701

    MATH  Google Scholar 

  28. Chutani S, Goyal A (2018) Improved universal quantitative steganalysis in spatial domain using ELM ensemble. Multimed Tools Appl 77(6):7447–7468

    Google Scholar 

  29. Chutani S, Goyal A (2019) A review of forensic approaches to digital image Steganalysis. Multimed Tools Appl 78(13):18169–18204

    Google Scholar 

  30. Dadgostar H, Afsari F (2016) Image steganography based on interval-valued intuitionistic fuzzy edge detection and modified LSB. J Inf Secur Appl 30:94–104

    Google Scholar 

  31. Dalal M, Juneja M (2018) Video steganalysis to obstruct criminal activities for digital forensics: a survey. Int J Electron Secur Digit Forensics 10(4):338–355

    Google Scholar 

  32. M Dalal and M Juneja (2018). “Video Steganography Techniques in Spatial Domain-A Survey,” in Proceedings of the International Conference on Computing and Communication Systems, Springer, Singapore, pp. 705–711

  33. Dalal M, Juneja M (2018) H. 264/AVC video steganography techniques: an overview. Int J Comput Sci Eng 6(5):297–303

    Google Scholar 

  34. Dalal M, Juneja M (2019) A robust and imperceptible steganography technique for SD and HD videos. Multimed Tools Appl 78(5):5769–5789

    Google Scholar 

  35. Dalal M, Juneja M (2020) Evaluation of orthogonal and biorthogonal wavelets for video steganography. Inf Secur J A Glob Perspect 29(1):1–11

    Google Scholar 

  36. Desai MB, Patel SV, Prajapati B (2016) ANOVA and fisher criterion based feature selection for lower dimensional universal image Steganalysis. Int J Image Process 10(3):145–160

    Google Scholar 

  37. V Divya and N Sasirekha (2016). “High capacity steganography technique based on wavelet transform,” in 2016 Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1–5

  38. Dogan S (2016) A new data hiding method based on chaos embedded genetic algorithm for color image. Artif Intell Rev 46(1):129–143

    Google Scholar 

  39. Duan X, Guo D, Liu N, Li B, Gou M, Qin C (2020) A new high capacity image steganography method combined with image elliptic curve cryptography and deep neural network. IEEE Access 8:25777–25788

    Google Scholar 

  40. Duan X, Jia K, Li B, Guo D, Zhang E, Qin C (2019) Reversible image steganography scheme based on a U-net structure. IEEE Access 7:9314–9323

    Google Scholar 

  41. Fan M, Liu P, Wang H, Sun X (2016) Cross correlation feature mining for steganalysis of hash based least significant bit substitution video steganography. Telecommun Syst:1–7

  42. Fan L, Sun W, Feng G (2019) Image steganalysis via random subspace fisher linear discriminant vector functional link network and feature mapping. Mob Networks Appl 24(4):1269–1278

    Google Scholar 

  43. T Filler and J Fridrich (2011). “Design of adaptive steganographic schemes for digital images,” in Media Watermarking, Security, and Forensics III, vol. 7880, p. 78800F

  44. Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7(3):868–882

    Google Scholar 

  45. Galiano DR, Del Barrio AA, Botella G, Cuesta D (2020) Efficient embedding and retrieval of information for high-resolution videos coded with HEVC. Comput Electr Eng 81:106541

    Google Scholar 

  46. S Gallagher (2012). “Steganography: how al-Qaeda hid secret documents in a porn video,”. [Online]. Available: https://arstechnica.com/business/2012/05/steganography-how-al-qaeda-hid-secret-documents-in-a-porn-video/. [Accessed: 01-Jul-2016]

  47. Gibouloto Q, Cogranneo R, Bas P (2018) Steganalysis into the wild: how to define a source? Electron Imaging 2018(7):311–318

    Google Scholar 

  48. M Goljan and J Fridrich (2015). “CFA-aware features for steganalysis of color images,” in Media Watermarking, Security, and Forensics 2015, vol. 9409, p. 94090V

  49. I. R. Grajeda-Marin, H. A. Montes-Venegas, J. R. Marcial-Romero, J. A. Hernández-Servin, and G. De Ita (2016). “An optimization approach to the TWPVD method for digital image steganography,” in Mexican Conference on Pattern Recognition, pp. 125–134

  50. Guo L, Ni J, Shi YQ (2014) Uniform embedding for efficient JPEG steganography. IEEE Trans Inf Forensics Secur 9(5):814–825

    Google Scholar 

  51. He Y, Yang G, Zhu N (2012) A real-time dual watermarking algorithm of H. 264/AVC video stream for video-on-demand service. AEU-International J Electron Commun 66(4):305–312

    Google Scholar 

  52. He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916

    Google Scholar 

  53. V Holub and J Fridrich (2012). “Designing steganographic distortion using directional filters,” in 2012 IEEE International workshop on information forensics and security (WIFS), pp. 234–239

  54. V Holub and J Fridrich (2013). “Digital image steganography using universal distortion,” in Proceedings of the first ACM workshop on Information hiding and multimedia security, pp. 59–68

  55. Hosam O, Ben Halima N (2016) Adaptive block-based pixel value differencing steganography. Secur Commun Networks 9(18):5036–5050

    Google Scholar 

  56. Hussain M, Abdul Wahab AW, Javed N, Jung K-H (2016) Hybrid data hiding scheme using right-most digit replacement and adaptive least significant bit for digital images. Symmetry (Basel) 8(6):41

    MathSciNet  Google Scholar 

  57. M Hussain, AWA Wahab, YI Bin Idris, ATS Ho, and K-H Jung (2018). “Image steganography in spatial domain: a survey,” Signal Process. Image Commun

  58. QP Huu, TH Dinh, NN Tran, TP Van, and TT Minh (2019). “Deep Neural Networks Based Invisible Steganography for Audio-into-Image Algorithm,” in The 8th Global Conference on Consumer Electronics (GCCE 2019), pp. 1–5

  59. T Idbeaa, SA Samad, and H Husain (2016). “A secure and robust compressed domain video steganography for intra-and inter-frames using embedding-based byte differencing (EBBD) scheme,” PLoS One, vol. 11, no. 3

  60. AK Jain and U Uludag (2002). “Hiding fingerprint minutiae in images,” in Proceedings of 3rd Workshop on Automatic Identification Advanced Technologies, pp. 97–102

  61. Jamil T (1999) Steganography: the art of hiding information in plain sight. IEEE potentials 18(1):10–12

    Google Scholar 

  62. Jan B, Farman H, Khan M, Imran M, Islam IU, Ahmad A, Ali S, Jeon G (2019) Deep learning in big data analytics: a comparative study. Comput Electr Eng 75:275–287

    Google Scholar 

  63. NF Johnson, Z Duric, and S Jajodia (2001). Information Hiding: Steganography and Watermarking-Attacks and Countermeasures: Steganography and Watermarking: Attacks and Countermeasures, vol. 1. Springer Science & Business Media

  64. Kadhim IJ, Premaratne P, Vial PJ (2020) Improved image steganography based on super-pixel and coefficient-plane-selection. Signal Process 171:107481

    Google Scholar 

  65. Kadhim IJ, Premaratne P, Vial PJ (2020) High capacity adaptive image steganography with cover region selection using dual-tree complex wavelet transform. Cogn Syst Res 60:20–32

    Google Scholar 

  66. I J Kadhim, P Premaratne, PJ Vial, and B Halloran (2017). “A comparative analysis among dual tree complex wavelet and other wavelet transforms based on image compression,” in International Conference on Intelligent Computing, pp. 569–580

  67. Kadhim IJ, Premaratne P, Vial PJ, Halloran B (2019) Comprehensive survey of image steganography: techniques, evaluations, and trends in future research. Neurocomputing 335:299–326

    Google Scholar 

  68. M Kalita and T Tuithung (2016). “A novel steganographic method using 8-neighboring PVD (8nPVD) and LSB substitution,” in 2016 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1–5

  69. Kang Y, Liu F, Yang C, Xiang L, Luo X, Wang P (2019) Color image steganalysis based on channel gradient correlation. Int. J. Distrib. Sens. Networks 15(5):1550147719852031

    Google Scholar 

  70. Katzenbeisser S, Petitcolas FAP (2000) Digital Watermarking. Artech House, London

    Google Scholar 

  71. M Kaur and M Juneja (2017). “Adaptive Block Based Steganographic Model with Dynamic Block Estimation with Fuzzy Rules,” in Innovations in Computer Science and Engineering, Springer, pp. 167–176

  72. Khan S, Bianchi T (2018) Ant colony optimization (aco) based data hiding in image complex region. Int J Electr Comput Eng 8(1):379–389

    Google Scholar 

  73. Khan H, Javed M, Khayam SA, Mirza F (2011) Designing a cluster-based covert channel to evade disk investigation and forensics. Comput Secur 30(1):35–49

    Google Scholar 

  74. Khodaei M, Sadeghi Bigham B, Faez K (2016) Adaptive data hiding, using pixel-value-differencing and LSB substitution. Cybern Syst 47(8):617–628

    Google Scholar 

  75. Kim J, Park H, Park J-I (2020) CNN-based image steganalysis using additional data embedding. Multimed Tools Appl 79(1–2):1355–1372

    Google Scholar 

  76. Kolakalur A, Kagalidis I, Vuksanovic B, Iacsit M (2016) Wavelet Based Color Video Steganography. Int. J. Eng. Technol 8(3):165

    Google Scholar 

  77. MZ Konyar, O Akbulut, and S Öztürk (2020). “Matrix encoding-based high-capacity and high-fidelity reversible data hiding in HEVC,” Signal, Image Video Process., pp. 1–9

  78. Koptyra K, Ogiela MR (2019) Multiply information coding and hiding using fuzzy vault. Soft Comput 23(12):4357–4366

    Google Scholar 

  79. Kumar V, Kumar D (2018) A modified DWT-based image steganography technique. Multimed Tools Appl 77(11):13279–13308

    Google Scholar 

  80. Kumar P, Singh K (2018) An improved data-hiding approach using skin-tone detection for video steganography. Multimed Tools Appl 77(18):24247–24268

    Google Scholar 

  81. Kuo W-C, Wang C-C, Hou H-C (2016) Signed digit data hiding scheme. Inf Process Lett 116(2):183–191

    MathSciNet  MATH  Google Scholar 

  82. Li Z, Meng L, Xu S, Li Z, Shi Y, Liang Y (2019) A HEVC video steganalysis algorithm based on pu partition modes. Comput Mater Contin 59(2):607–624

    Google Scholar 

  83. Li B, Wei W, Ferreira A, Tan S (2018) ReST-net: diverse activation modules and parallel subnets-based CNN for spatial image steganalysis. IEEE Signal Process Lett 25(5):650–654

    Google Scholar 

  84. Liao X, Guo S, Yin J, Wang H, Li X, Sangaiah AK (2018) New cubic reference table based image steganography. Multimed Tools Appl 77(8):10033–10050

    Google Scholar 

  85. Liu Y, Ju L, Hu M, Zhao H, Jia S, Jia Z (2016) A new data hiding method for H.264 based on secret sharing. Neurocomputing 188:113–119

    Google Scholar 

  86. Liu H-H, Lee C-M (2019) High-capacity reversible image steganography based on pixel value ordering. EURASIP J. Image Video Process 2019(1):54

    Google Scholar 

  87. Liu P, Li S (2020) Steganalysis of Intra Prediction Mode and Motion Vector-based Steganography by Noise Residual Convolutional Neural Network. IOP Conference Series: Materials Science and Engineering 719(1):12068

    Google Scholar 

  88. Liu Y, Liu S, Zhao H, Liu S (2018) A new data hiding method for H. 265/HEVC video streams without intra-frame distortion drift. Multimed Tools Appl:1–28

  89. Liu Y, Qu X, Xin G (2016) A ROI-based reversible data hiding scheme in encrypted medical images. J Vis Commun Image Represent 39:51–57

    Google Scholar 

  90. W Lu, R Li, L Zeng, J Chen, J Huang, and Y-Q Shi (2019). “Binary image steganalysis based on histogram of structuring elements,” IEEE Trans. Circuits Syst. Video Technol

  91. Lubacz J, Mazurczyk W, Szczypiorski K (2014) Principles and overview of network steganography. IEEE Commun Mag 52(5):225–229

    Google Scholar 

  92. Luo T, Jiang G, Yu M, Xu H, Gao W (2018) Sparse recovery based reversible data hiding method using the human visual system. Multimed Tools Appl 77(15):19027–19050

    Google Scholar 

  93. G Manikandan, R Bala Krishnan, N Rajesh Kumar, N Sairam, and NR Raajan (2017). “A steganographic approach for realizing medical data privacy in a distributed environment,” Biomed. Res., vol. 28, no. 3

  94. Manisha S, Sharmila TS (2019) A two-level secure data hiding algorithm for video steganography. Multidim Syst Sign Process 30(2):529–542

    MATH  Google Scholar 

  95. W Mazurczyk, M Karas, and K Szczypiorski (2013). “SkyDe: a Skype-based steganographic method,” arXiv Prepr. arXiv1301.3632

  96. Mazurczyk W, Wendzel S (2017) Information hiding: challenges for forensic experts. Commun ACM 61(1):86–94

    Google Scholar 

  97. Mercuri RT (2004) The many colors of multimedia security. Commun ACM 47(12):25–29

    Google Scholar 

  98. Miri A, Faez K (2017) Adaptive image steganography based on transform domain via genetic algorithm. Optik (Stuttg) 145:158–168

    Google Scholar 

  99. Miri A, Faez K (2018) An image steganography method based on integer wavelet transform. Multimed Tools Appl 77(11):13133–13144

    Google Scholar 

  100. Mousavi SM, Naghsh A, Abu-Bakar SAR (2014) Watermarking techniques used in medical images: a survey. J Digit Imaging 27(6):714–729

    Google Scholar 

  101. Mstafa RJ, Elleithy KM (2016) A video steganography algorithm based on Kanade-Lucas-Tomasi tracking algorithm and error correcting codes. Multimed Tools Appl 75(17):10311–10333

    Google Scholar 

  102. RJ Mstafa and KM Elleithy (2016). “A DCT-based robust video steganographic method using BCH error correcting codes,” in 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT), pp. 1–6

  103. Mstafa RJ, Elleithy KM, Abdelfattah E (2017) A robust and secure video steganography method in DWT-DCT domains based on multiple object tracking and ECC. IEEE Access 5:5354–5365

    Google Scholar 

  104. RJ Mstafa and KM Ellleithy (2016). “An Efficient Video Steganography Algorithm Based on BCH Codes,” ASEE, no. May 2015

  105. Muhammad K, Ahmad J, Rehman NU, Jan Z, Sajjad M (2017) CISSKA-LSB: color image steganography using stego key-directed adaptive LSB substitution method. Multimed Tools Appl 76(6):8597–8626

    Google Scholar 

  106. Muhammad K, Ahmad J, Rho S, Baik SW (2017) Image steganography for authenticity of visual contents in social networks. Multimed Tools Appl 76(18):18985–19004

    Google Scholar 

  107. Muhammad K, Sajjad M, Baik SW (2016) Dual-level security based cyclic18 steganographic method and its application for secure transmission of keyframes during wireless capsule endoscopy. J. Med. Syst. 40(5):114

    Google Scholar 

  108. Muhammad K, Sajjad M, Mehmood I, Rho S, Baik SW (2016) A novel magic LSB substitution method (M-LSB-SM) using multi-level encryption and achromatic component of an image. Multimed Tools Appl 75(22):14867–14893

    Google Scholar 

  109. Mungmode S, Sedamkar RR, Kulkarni N (2016) A modified high frequency adaptive security approach using steganography for region selection based on threshold value. Procedia Comput Sci 79:912–921

    Google Scholar 

  110. Neuner S, Voyiatzis AG, Schmiedecker M, Brunthaler S, Katzenbeisser S, Weippl ER (2016) Time is on my side: steganography in filesystem metadata. Digit Investig 18:S76–S86

    Google Scholar 

  111. Nie Q, Weng J, Xu X, Feng B (2018) Defining embedding distortion for intra prediction mode-based video steganography. Comput Mater Contin 55:59–70

    Google Scholar 

  112. H Nyeem (2017). “Reversible data hiding with image bit-plane slicing,” in 2017 20th International Conference of Computer and Information Technology (ICCIT), pp. 1–6

  113. F Pan, L Xiang, X Yang, and Y Guo (2010). “Video steganography using motion vector and linear block codes,” 2010 IEEE Int. Conf. Softw. Eng. Serv. Sci., no. 60842006, pp. 592–595

  114. HN Patel, DR Khant, and D Prajapati (2017). “Design of a color palette based image steganography algorithm for fractal images,” in 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 2584–2589

  115. Patel A, Shah M, Chandramouli R, Subbalakshmi KP (2007) Covert channel forensics on the internet: issues, approaches, and experiences. IJ Netw Secur 5(1):41–50

    Google Scholar 

  116. Perumal K, Muthusamy S, Gengavel G (2018) Sparse data encoder and decoder to improve security in video steganography. Concurr Comput Pract Exp:1–7

  117. Pevny T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224

    Google Scholar 

  118. T Pevny, T Filler, and P Bas (2010). “Using high-dimensional image models to perform highly undetectable steganography,” in International Workshop on Information Hiding, pp. 161–177

  119. B Pfitzmann (1996). “Information hiding terminology-results of an informal plenary meeting and additional proposals,” in Proceedings of the First International Workshop on Information Hiding, pp. 347–350

  120. Pinson MH, Wolf S (2004) A new standardized method for objectively measuring video quality. IEEE Trans Broadcast 50(3):312–322

    Google Scholar 

  121. V Pomponiu, D Cavagnino, and M Botta (2018). “Data Hiding in the Wild: Where Computational Intelligence Meets Digital Forensics,” in Surveillance in Action, Springer, pp. 301–331

  122. Y Qian, J Dong, W Wang, and T Tan (2016). “Learning and transferring representations for image steganalysis using convolutional neural network,” in 2016 IEEE international conference on image processing (ICIP), pp. 2752–2756

  123. Qu Z, Cheng Z, Wang X (2019) Matrix coding-based quantum image steganography algorithm. IEEE Access 7:35684–35698

    Google Scholar 

  124. Rabie T, Baziyad M (2019) The Pixogram: addressing high payload demands for video steganography. IEEE Access 7:21948–21962

    Google Scholar 

  125. Rabie T, Kamel I (2017) High-capacity steganography: a global-adaptive-region discrete cosine transform approach. Multimed Tools Appl 76(5):6473–6493

    Google Scholar 

  126. R Rahim, S Nadeem, and others (2018). “End-to-End Trained CNN Encoder-Decoder Networks for Image Steganography,” in Proceedings of the European Conference on Computer Vision (ECCV), p. 0

  127. Rajalakshmi K, Mahesh K (2018) ZLBM: zero level binary mapping technique for video security. Multimed Tools Appl 77(11):13225–13247

    Google Scholar 

  128. Rajendran S, Doraipandian M (2017) Chaotic map based random image steganography using LSB technique. IJ Netw Secur 19(4):593–598

    Google Scholar 

  129. Ramalingam M, Ashidi N, Isa M (2015) Fast retrieval of hidden data using enhanced hidden Markov model in video steganography. Appl Soft Comput J 34:744–757

    Google Scholar 

  130. Ramalingam M, Isa NAM (2016) A data-hiding technique using scene-change detection for video steganography. Comput Electr Eng 54:423–434

    Google Scholar 

  131. Y Ren, L Zhai, L Wang, and T Zhu (2014). “Video steganalysis based on subtractive probability of optimal matching feature,” in Proceedings of the 2nd ACM workshop on Information hiding and multimedia security, pp. 83–90

  132. K Rezagholipour and M Eshghi (2016). “Video Steganography Algorithm based on motion vector of moving object,” in Information and Knowledge Technology (IKT), 2016 Eighth International Conference on, pp. 183–187

  133. IE Richardson (2004). H. 264 and MPEG-4 video compression: video coding for next-generation multimedia. Wiley

  134. Rocha A, Scheirer W, Boult T, Goldenstein S (2011) Vision of the unseen: Current trends and challenges in digital image and video forensics. ACM Comput. Surv 43(4):26

    Google Scholar 

  135. Sadat ES, Faez K, Saffari Pour M (2018) Entropy-based video Steganalysis of motion vectors. Entropy 20(4):244–257

    Google Scholar 

  136. Sadek MM, Khalifa AS, Mostafa MGM (2017) Robust video steganography algorithm using adaptive skin-tone detection. Multimed Tools Appl 76(2):3065–3085

    Google Scholar 

  137. Sahu AK, Swain G (2020) Reversible image steganography using dual-layer LSB matching. Sens. Imaging 21(1):1

    Google Scholar 

  138. Saidi M, Hermassi H, Rhouma R, Belghith S (2017) A new adaptive image steganography scheme based on DCT and chaotic map. Multimed Tools Appl 76(11):13493–13510

    Google Scholar 

  139. S Sajasi and AME Moghadam (2013). “A high quality image steganography scheme based on fuzzy inference system,” in 2013 13th Iranian Conference on Fuzzy Systems (IFSC), pp. 1–6

  140. N Sathisha, R Priya, KS Babu, KB Raja, KR Venugopal, and LM Patnaik (2013). “DTCWT based high capacity steganography using coefficient replacement and adaptive scaling,” in Sixth International Conference on Machine Vision (ICMV 2013), vol. 9067, p. 90671O

  141. G Savithri, S Mane, JS Banu, and others (2017). “Parallel Implementation of RSA 2D-DCT Steganography and Chaotic 2D-DCT Steganography,” in Proceedings of International Conference on Computer Vision and Image Processing, pp. 593–605

  142. Shafi I, Noman M, Gohar M, Ahmad A, Khan M, Din S, Ahmad SH, Ahmad J (2018) An adaptive hybrid fuzzy-wavelet approach for image steganography using bit reduction and pixel adjustment. Soft Comput 22(5):1555–1567

    Google Scholar 

  143. PD Shah and RS Bichkar (2018). “A secure spatial domain image steganography using genetic algorithm and linear congruential generator,” in International Conference on Intelligent Computing and Applications, pp. 119–129

  144. Sheng Q, Wang RD, Huang ML, Li Q, Xu DW (2017) A prediction mode steganalysis detection algorithm for hevc. J Opt 28(4):433–440

    Google Scholar 

  145. D Singla and M Juneja (2014). “An analysis of edge based image steganography techniques in spatial domain,” in 2014 Recent Advances in Engineering and Computational Sciences (RAECS), pp. 1–5

  146. Singla D, Juneja M (2014) New information hiding technique using features of image. J Emerg Technol Web Intell 6(2):237–242

    Google Scholar 

  147. X Song, Z Li, L Chen, and J Liu (2016). “Entropy feature based on 2D Gabor wavelets for JPEG steganalysis,” in International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, pp. 59–72

  148. X Song, F Liu, C Yang, X Luo, and Y Zhang (2015). “Steganalysis of adaptive JPEG steganography using 2D Gabor filters,” in Proceedings of the 3rd ACM workshop on information hiding and multimedia security, pp. 15–23

  149. C Stier (2010). “Russian spy ring hid secret messages on the web,”. [Online]. Available: https://www.newscientist.com/article/dn19126-russian-spy-ring-hid-secret-messages-on-the-web/. [Accessed: 11-Jan-2017]

  150. T Stütz, F Autrusseau, and A Uhl (2013). “Inter-frame H. 264/CAVLC structure-preserving substitution watermarking

  151. Su Y, Yu F, Zhang C (2017) Digital video Steganalysis based on a spatial temporal detector. TIIS 11(1):360–373

    Google Scholar 

  152. Subhedar MS, Mankar VH (2014) Current status and key issues in image steganography: a survey. Comput Sci Rev 13:95–113

    MATH  Google Scholar 

  153. Subhedar MS, Mankar VH (2016) Image steganography using redundant discrete wavelet transform and QR factorization. Comput Electr Eng 54:406–422

    Google Scholar 

  154. Subhedar MS, Mankar VH (2018) Curvelet transform and cover selection for secure steganography. Multimed Tools Appl 77(7):8115–8138

    Google Scholar 

  155. Sudeepa KB, Raju K, HS RK, Aithal G (2016) A new approach for video steganography based on randomization and parallelization. Procedia Comput Sci 78:483–490

    Google Scholar 

  156. Suttichaiya A, Sombatkiripaiboon Y, Imtongkhua P, Poonriboon C, So-In C, Horkaew P (2017) Video steganography with LSB color detection. J Telecommun Electron Comput Eng 9(2–2):23–28

    Google Scholar 

  157. Swain G (2016) A steganographic method combining LSB substitution and PVD in a block. Procedia Comput Sci 85:39–44

    Google Scholar 

  158. Tang M, Hu J, Song W (2014) A high capacity image steganography using multi-layer embedding. Optik (Stuttg) 125(15):3972–3976

    Google Scholar 

  159. Tang W, Li B, Tan S, Barni M, Huang J (2019) CNN-based adversarial embedding for image steganography. IEEE Trans Inf Forensics Secur 14(8):2074–2087

    Google Scholar 

  160. Tang W, Tan S, Li B, Huang J (2017) Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Process Lett 24(10):1547–1551

    Google Scholar 

  161. Tanwar R, Malhotrab S (2017) Scope of Support Vector Machine in Steganography. IOP Conf. Series: Materials and Engineering 225:12077

    Google Scholar 

  162. Tasdemir K, Kurugollu F, Sezer S (2016) Spatio-temporal rich model-based video Steganalysis on cross sections of motion vector Planes. IEEE Trans Image Process 25(7):3316–3328

    MathSciNet  MATH  Google Scholar 

  163. Tavares R, Madeiro F (2016) Word-hunt: a LSB steganography method with low expected number of modifications per pixel. IEEE Lat Am Trans 14(2):1058–1064

    Google Scholar 

  164. Thomee B, Shamma DA, Friedland G, Elizalde B, Ni K, Poland D, Borth D, Li L-J (2016) YFCC100M: the new data in multimedia research. Commun ACM 59(2):64–73

    Google Scholar 

  165. MRN Torkaman, P Nikfard, NS Kazazi, MR Abbasy, and SF Tabatabaiee (2011). “Improving hybrid cryptosystems with DNA steganography,” in International Conference on Digital Enterprise and Information Systems, pp. 42–52

  166. Tsang CF, Fridrich J (2018) Steganalyzing images of arbitrary size with CNNs. Electron. Imaging 2018(7):121

    Google Scholar 

  167. R Umadevi (2016). “Joint approach for secure communication using video steganography: Achieving better communication based on video steganography,” in 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 3104–3106

  168. Veena ST, Arivazhagan S (2018) Quantitative steganalysis of spatial LSB based stego images using reduced instances and features. Pattern Recogn Lett 105:39–49

    Google Scholar 

  169. D Volkhonskiy, I Nazarov, and E Burnaev (2020). “Steganographic generative adversarial networks,” in Twelfth International Conference on Machine Vision (ICMV 2019), vol. 11433, p. 114333M

  170. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Google Scholar 

  171. Wang P, Cao Y, Zhao X (2017) Segmentation based video Steganalysis to detect motion vector modification. Secur Commun Networks 2017:1–12

    Google Scholar 

  172. Y Wang, Y Cao, X Zhao, Z Xu, and M Zhu (2018). “Maintaining Rate-Distortion Optimization for IPM-Based Video Steganography by Constructing Isolated Channels in HEVC,” in Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security, pp. 97–107

  173. Z Wang, X Zhao, H Wang, and G Cui (2013). “Information hiding based on DNA steganography,” in 2013 IEEE 4th International Conference on Software Engineering and Service Science, pp. 946–949

  174. Warkentin M, Bekkering E, Schmidt MB (2008) Steganography: Forensic, Security, and Legal Issues. J. Digit. Forensics, Secur. Law JDFSL 3(2):17

    Google Scholar 

  175. C-Y Weng, C-T Huang, and H-W Kao (2017). “DCT-based compressed image with reversibility using modified quantization,” in International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 214–221

  176. E Wengrowski and K Dana (2019). “Light Field Messaging With Deep Photographic Steganography,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1515–1524

  177. C Whitelam, N Osia, and T Bourlai (2013). “Securing multimodal biometric data through watermarking and steganography,” in 2013 IEEE International Conference on Technologies for Homeland Security (HST), pp. 61–66

  178. Wiegand T, Sullivan GJ, Bjontegaard G, Luthra A (2003) Overview of the H. 264/AVC video coding standard. IEEE Trans circuits Syst video Technol 13(7):560–576

    Google Scholar 

  179. C. Xu, X. Ping, and T. Zhang, “Steganography in compressed video stream,” in Innovative Computing, Information and Control, 2006. ICICIC’06. First international conference on, 2006, vol. 1, pp. 269–272.

  180. Xu D, Wang R, Shi YQ (2016) An improved scheme for data hiding in encrypted H.264/AVC videos. J Vis Commun Image Represent 36:229–242

    Google Scholar 

  181. Xu G, Wu H-Z, Shi Y-Q (2016) Structural design of convolutional neural networks for steganalysis. IEEE Signal Process Lett 23(5):708–712

    Google Scholar 

  182. G Xu, H-Z Wu, and YQ Shi (2016). “Ensemble of CNNs for steganalysis: An empirical study,” in Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, pp. 103–107

  183. Xue Y, Liu W, Lu W, Yeung Y, Liu X, Liu H (2019) Efficient halftone image steganography based on dispersion degree optimization. J Real-Time Image Process 16(3):601–609

    Google Scholar 

  184. Xue Y, Zhou J, Zeng H, Zhong P, Wen J (2019) An adaptive steganographic scheme for H. 264/AVC video with distortion optimization. Signal Process Image Commun 76:22–30

    Google Scholar 

  185. Yang C-N, Chen T-S, Yu KH, Wang C-C (2007) Improvements of image sharing with steganography and authentication. J Syst Softw 80(7):1070–1076

    Google Scholar 

  186. Yang C, Kang Y, Liu F, Song X, Wang J, Luo X (2020) Color image steganalysis based on embedding change probabilities in differential channels. Int. J. Distrib. Sens. Networks 16(5):1550147720917826

    Google Scholar 

  187. Yang C, Luo X, Lu J, Liu F (2018) Extracting hidden messages of MLSB steganography based on optimal stego subset. Sci. China Inf. Sci 61(11):119103

    Google Scholar 

  188. Yao Y, Zhang W, Yu N (2016) Inter-frame distortion drift analysis for reversible data hiding in encrypted H.264/AVC video bitstreams. Signal Process 128:531–545

    Google Scholar 

  189. Yao Y, Zhang W, Yu N, Zhao X (2015) Defining embedding distortion for motion vector-based video steganography. Multimed Tools Appl 74(24):11163–11186

    Google Scholar 

  190. Ye J, Ni J, Yi Y (2017) Deep learning hierarchical representations for image steganalysis. IEEE Trans Inf Forensics Secur 12(11):2545–2557

    Google Scholar 

  191. M Yedroudj, F Comby, and M Chaumont (2018). “Yedroudj-net: An efficient CNN for spatial steganalysis,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2092–2096

  192. Zarmehi N, Akhaee MA (2016) Digital video steganalysis toward spread spectrum data hiding. IET Image Process 10(1):1–8

    Google Scholar 

  193. Zeng J, Tan S, Li B, Huang J (2017) Large-scale JPEG image steganalysis using hybrid deep-learning framework. IEEE Trans Inf Forensics Secur 13(5):1200–1214

    Google Scholar 

  194. L Zhai, L Wang, and Y Ren (2019). “Universal detection of video steganography in multiple domains based on the consistency of motion vectors,” IEEE Trans. Inf. Forensics Secur

  195. Zhang X (2011) Reversible data hiding in encrypted image. IEEE Signal Process Lett 18(4):255–258

    Google Scholar 

  196. Zhang R, Dong S, Liu J (2019) Invisible steganography via generative adversarial networks. Multimed Tools Appl 78(7):8559–8575

    Google Scholar 

  197. Zhang X, Peng F, Long M (2018) Robust coverless image steganography based on DCT and LDA topic classification. IEEE Trans Multimed 20(12):3223–3238

    Google Scholar 

  198. Zhang T, Ping X (2003) A new approach to reliable detection of LSB steganography in natural images. Signal Process 83(10):2085–2093

    MATH  Google Scholar 

  199. Zhang T, Zhang H, Wang R, Wu Y (2019) A new JPEG image steganalysis technique combining rich model features and convolutional neural networks. Math Biosci Eng 16(5):4069–4081

    Google Scholar 

  200. R Zhang, F Zhu, J Liu, and G Liu (2018). “Efficient feature learning and multi-size image steganalysis based on CNN,” arXiv Prepr. arXiv1807.11428

  201. Y Zhao, H Zhang, Y Cao, P Wang, and X Zhao (2015). “Video Steganalysis Based on Intra Prediction Mode Calibration,” in International Workshop on Digital Watermarking, pp. 119–133

Download references

Acknowledgements

This research work is supported by Technical Education Quality Improvement Project III (TEQIP III) of MHRD, Government of India assisted by World Bank under Grant Number P154523 and sanctioned to UIET, Panjab University, Chandigarh (India).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mukesh Dalal.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dalal, M., Juneja, M. Steganography and Steganalysis (in digital forensics): a Cybersecurity guide. Multimed Tools Appl 80, 5723–5771 (2021). https://doi.org/10.1007/s11042-020-09929-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09929-9

Keywords

Navigation