Skip to main content
Log in

A video steganography algorithm based on Kanade-Lucas-Tomasi tracking algorithm and error correcting codes

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

Abstract

Due to the significant growth of video data over the Internet, video steganography has become a popular choice. The effectiveness of any steganographic algorithm depends on the embedding efficiency, embedding payload, and robustness against attackers. The lack of the preprocessing stage, less security, and low quality of stego videos are the major issues of many existing steganographic methods. The preprocessing stage includes the procedure of manipulating both secret data and cover videos prior to the embedding stage. In this paper, we address these problems by proposing a novel video steganographic method based on Kanade-Lucas-Tomasi (KLT) tracking using Hamming codes (15, 11). The proposed method consists of four main stages: a) the secret message is preprocessed using Hamming codes (15, 11), producing an encoded message, b) face detection and tracking are performed on the cover videos, determining the region of interest (ROI), defined as facial regions, c) the encoded secret message is embedded using an adaptive LSB substitution method in the ROIs of video frames. In each facial pixel 1 LSB, 2 LSBs, 3 LSBs, and 4 LSBs are utilized to embed 3, 6, 9, and 12 bits of the secret message, respectively, and d) the process of extracting the secret message from the RGB color components of the facial regions of stego video is executed. Experimental results demonstrate that the proposed method achieves higher embedding capacity as well as better visual quality of stego videos. Furthermore, the two preprocessing steps increase the security and robustness of the proposed algorithm as compared to state-of-the-art methods.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Alavianmehr MA, Rezaei M, Helfroush MS, Tashk A (2012) A lossless data hiding scheme on video raw data robust against H.264/AVC compression. In: Computer and Knowledge Engineering (ICCKE), 2012 2nd International eConference on 194–198. doi:10.1109/iccke.2012.6395377

  2. Bhole AT, Patel R (2012) Steganography over video file using Random Byte Hiding and LSB technique. In: Computational Intelligence & Computing Research (ICCIC. IEEE Int Conf 1–6. doi:10.1109/iccic.2012.6510230

  3. Cheddad A, Condell J, Curran K, Mc Kevitt P (2009) A secure and improved self-embedding algorithm to combat digital document forgery. Sig Process 89(12):2324–2332. doi:10.1016/j.sigpro.2009.02.001

    Article  MATH  Google Scholar 

  4. Cheddad A, Condell J, Curran K, Mc Kevitt P (2009) A skin tone detection algorithm for an adaptive approach to steganography. Sig Process 89(12):2465–2478. doi:10.1016/j.sigpro.2009.04.022

    Article  MATH  Google Scholar 

  5. Cheddad A, Condell J, Curran K, Mc Kevitt P (2010) Digital image steganography: survey and analysis of current methods. Sig Process 90(3):727–752. doi:10.1016/j.sigpro.2009.08.010

    Article  MATH  Google Scholar 

  6. Cheddad A, Condell J, Curran K, McKevitt P (2008) Skin tone based Steganography in video files exploiting the YCbCr colour space. In: Multimedia and Expo. IEEE Int Conf 905–908. doi:10.1109/icme.2008.4607582

  7. Chin-Chen C, Kieu TD, Yung-Chen C (2008) A high payload steganographic scheme based on (7, 4) hamming code for digital images. In: Electronic Commerce and Security. Int Symp 16–21. doi:10.1109/isecs.2008.222

  8. Egiazarian K, Astola J, Ponomarenko N, Lukin V, Battisti F, Carli M (2006) New full-reference quality metrics based on HVS. In: CD-ROM proceedings of the second international workshop on video processing and quality metrics, Scottsdale, USA

  9. Farschi S, Farschi H (2012) A novel chaotic approach for information hiding in image. Nonlinear Dyn 69(4):1525–1539. doi:10.1007/s11071-012-0367-5

    Article  MathSciNet  Google Scholar 

  10. Fassold H, Rosner J, Schallauer P, Bailer W (2009) Realtime KLT feature point tracking for high definition video. GraVisMa

  11. Fontaine C, Galand F (2007) How Can Reed-Solomon Codes Improve Steganographic Schemes? In: Furon T, Cayre F, Doërr G, Bas P (eds) Information Hiding, vol 4567. Lect Notes Comput Sci. Springer Berlin Heidelberg, pp 130–144. doi:10.1007/978-3-540-77370-2_9

  12. Guangjie L, Weiwei L, Yuewei D, Shiguo L (2011) An adaptive matrix embedding for image steganography. In: Multimedia Information Networking and Security (MINES). Third Int Conf 642–646. doi:10.1109/mines.2011.138

  13. Guangjie L, Weiwei L, Yuewei D, Shiguo L (2012) Adaptive steganography based on syndrome-trellis codes and local complexity. In: Multimedia Information Networking and Security (MINES). Fourth Int Conf 323–327. doi:10.1109/mines.2012.55

  14. Guo-Shiang L, Tung-Sheng T (2013) A face tracking method using feature point tracking. In: Information Security and Intelligence Control (ISIC). Int Conf 210–213. doi:10.1109/isic.2012.6449743

  15. Hasnaoui M, Mitrea M (2014) Multi-symbol QIM video watermarking. Sig Process Image Commun 29(1):107–127. doi:10.1016/j.image.2013.07.007

    Article  Google Scholar 

  16. 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 Int J Electron Commun 66(4):305–312. doi:10.1016/j.aeue.2011.08.007

    Article  Google Scholar 

  17. Islam S, Modi MR, Gupta P (2014) Edge-based image steganography. EURASIP J Inf Secur 2014(1):1–14

    Article  Google Scholar 

  18. Isukapalli R, Elgammal A, Greiner R (2005) Learning a dynamic classification method to detect faces and identify facial expression. In: Zhao W, Gong S, Tang X (eds) Analysis and Modelling of Faces and Gestures, vol 3723. Lect Notes Comput Sci. Springer Berlin Heidelberg, pp 70–84. doi:10.1007/11564386_7

  19. Kelash HM, Abdel Wahab OF, Elshakankiry OA, El-sayed HS (2013) Hiding data in video sequences using steganography algorithms. In: ICT Convergence (ICTC). Int Conf 353–358. doi:10.1109/ictc.2013.6675372

  20. Khupse S, Patil NN (2014) An adaptive steganography technique for videos using Steganoflage. In: Issues and Challenges in Intelligent Computing Techniques (ICICT). Int Conf 811–815. doi:10.1109/icicict.2014.6781384

  21. Leibe B, Leonardis A, Schiele B (2008) Robust object detection with interleaved categorization and segmentation. Int J Comput Vis 77(1–3):259–289. doi:10.1007/s11263-007-0095-3

    Article  Google Scholar 

  22. Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: IJCAI, 674–679

  23. Lusson F, Bailey K, Leeney M, Curran K (2013) A novel approach to digital watermarking, exploiting colour spaces. Sig Process 93(5):1268––1294. doi:10.1016/j.sigpro.2012.10.018

    Article  Google Scholar 

  24. Masoumi M, Amiri S (2013) A blind scene-based watermarking for video copyright protection. AEU Int J Electron Commun 67(6):528–535. doi:10.1016/j.aeue.2012.11.009

    Article  Google Scholar 

  25. Moon SK, Raut RD (2013) Analysis of secured video steganography using computer forensics technique for enhance data security. In: Image Information Processing (ICIIP). IEEE Second Int Conf 660–665. doi:10.1109/iciip.2013.6707677

  26. Mstafa RJ, Elleithy KM (2014) A highly secure video steganography using Hamming code (7, 4). In: Systems, Applications and Technology Conference (LISAT). IEEE Long Island 1–6. doi:10.1109/lisat.2014.6845191

  27. Mstafa RJ, Elleithy KM (2015) A high payload video steganography algorithm in DWT domain based on BCH codes (15, 11). In: Wireless Telecommunications Symposium (WTS). 1–8. doi:10.1109/wts.2015.7117257

  28. Mstafa RJ, Elleithy KM (2015) A novel video steganography algorithm in the wavelet domain based on the KLT tracking algorithm and BCH codes. In: Systems, Applications and Technology Conference (LISAT). IEEE Long Island 1–7. doi:10.1109/lisat.2015.7160192

  29. Paul R, Acharya AK, Yadav VK, Batham S (2013) Hiding large amount of data using a new approach of video steganography. In: Confluence 2013: The Next Generation Information Technology Summit (4th International Conference) 337–343. doi:10.1049/cp.2013.2338

  30. Ponomarenko N, Silvestri F, Egiazarian K, Carli M, Astola J, Lukin V (2007) On between-coefficient contrast masking of DCT basis functions. In: Proceedings of the Third International Workshop on Video Processing and Quality Metrics

  31. Qazanfari K, Safabakhsh R (2014) A new steganography method which preserves histogram: Generalization of LSB++. Inform Sci 277:90–101. doi:10.1016/j.ins.2014.02.007

    Article  MathSciNet  Google Scholar 

  32. Qian Z, Feng G, Zhang X, Wang S (2011) Image self-embedding with high-quality restoration capability. Digit Sig Process 21(2):278–286. doi:10.1016/j.dsp.2010.04.006

    Article  MathSciNet  Google Scholar 

  33. Rupa C (2013) A digital image steganography using sierpinski gasket fractal and PLSB. J Inst Eng India B 94(3):147–151. doi:10.1007/s40031-013-0054-z

    Article  Google Scholar 

  34. Sadek M, Khalifa A, Mostafa MM (2014) Video steganography: a comprehensive review. Multimed Tools 1–32. doi:10.1007/s11042-014-1952-z

  35. Sarkar A, Madhow U, Manjunath BS (2010) Matrix embedding with pseudorandom coefficient selection and error correction for robust and secure steganography. IEEE Trans Inf Forensics Secur 5(2):225–239. doi:10.1109/tifs.2010.2046218

    Article  Google Scholar 

  36. Shi J, Tomasi C (1994) Good features to track. Proc IEEE Conf Comput Vis Pattern Recognit Comput Soc 593–600. doi:10.1109/cvpr.1994.323794

  37. Subhedar MS, Mankar VH (2014) Current status and key issues in image steganography: a survey. Comput Sci Rev 13–14:95–113. doi:10.1016/j.cosrev.2014.09.001cbrs

    Article  MATH  Google Scholar 

  38. Tomasi C, Kanade T (1991) Detection and tracking of point features. School of Computer Science, Carnegie Mellon Univ, Pittsburgh

    Google Scholar 

  39. Torres-Pereira E, Martins-Gomes H, Monteiro-Brito A, de Carvalho J (2014) Hybrid Parallel Cascade Classifier Training for Object Detection. In: Bayro-Corrochano E, Hancock E (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, vol 8827. Lect Notes Comput Sci. Springer International Publishing, pp 810–817. doi:10.1007/978-3-319-12568-8_98

  40. Tse-Hua L, Tewfik AH (2006) A novel high-capacity data-embedding system. IEEE Trans Image Process 15(8):2431–2440. doi:10.1109/tip.2006.875238

    Article  Google Scholar 

  41. Viola P, Jones M (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154. doi:10.1023/B:VISI.0000013087.49260.fb

    Article  Google Scholar 

  42. Viola P, Jones M Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings IEEE Comput Soc Conf 511:I-511-I-518. doi:10.1109/cvpr.2001.990517

  43. Wang X-y, Wang C-p, Yang H-y, Niu P-p (2013) A robust blind color image watermarking in quaternion Fourier transform domain. J Syst Softw 86(2):255–277. doi:10.1016/j.jss.2012.08.015

    Article  Google Scholar 

  44. Yiqi T, KokSheik W (2014) An overview of information hiding in H.264/AVC compressed video. IEEE Trans Circuits Syst Video Technol 24(2):305–319. doi:10.1109/tcsvt.2013.2276710

    Article  Google Scholar 

  45. Zhang R, Sachnev V, Kim H (2009) Fast BCH syndrome coding for steganography. In: Katzenbeisser S, Sadeghi A-R (eds) Information Hiding, vol 5806. Lect Notes Comput Sci. Springer Berlin Heidelberg, pp 48–58. doi:10.1007/978-3-642-04431-1_4

Download references

Acknowledgments

The authors are sincerely thankful to the associate editor and anonymous reviewers for their useful suggestions and constructive comments which improved the quality of our research work. We are also grateful to Ms. Camy Deck of English department, University of Bridgeport, Bridgeport, USA for proofreading of our work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ramadhan J. Mstafa.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mstafa, R.J., Elleithy, K.M. A video steganography algorithm based on Kanade-Lucas-Tomasi tracking algorithm and error correcting codes. Multimed Tools Appl 75, 10311–10333 (2016). https://doi.org/10.1007/s11042-015-3060-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-3060-0

Keywords

Navigation