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A novel approach of data hiding in video using region selection and PCA

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Abstract

With the ubiquitous progress of information technology it is now possible to transfer multimedia information rapidly over the Internet. Significant growth of video data on the Internet insists the users towards video steganography as a popular choice for data hiding. Steganography algorithm must emphasis to improve the embedding efficiency, payload and robustness against the intruders. In this paper, we have addressed those issues and present a new approach of steganography. Our segmentation process is based on video frames. We apply a region selection method followed by the dimensionality reduction process, called principal component analysis (PCA), to compress the regions and embed secret data on those compact regions. This PCA is used as a best-fitted vector that minimizes the average square distance from the pixel values to that vector. Our results show higher embedding capacity along with better visual quality. Moreover, the proposed method improves the robustness in the sense that the secret message can be retrieved by the receiver even after some known attacks on the channel.

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Notes

  1. The error in the channel is govourned by some stastistical distribution

Abbreviations

BER:

Bit Error Rate

CR:

Compression ratio

DCT:

Discrete cosine transform

DWT:

Discrete wavelet transform

LSB:

Least significant bit

MAE:

Mean absolute error

MOT:

Multiple object tracking algorithm

MPE:

Mean percentage error

MSE:

Mean Square Error

PCA:

Principal component analysis

PSNR:

Peak signal-to-noise ratio

SF:

Similarity function

References

  1. Al-Juaid NA, Gutub AA, Khan EA (2018) Enhancing PC data security via combining RSA cryptography and video based steganography. J Inform Secur Cybercrimes Res 1(1). https://doi.org/10.26735/16587790.2018.006

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

  3. Cetin O, Akar F, Ozcerit AT, Cakiroglu M, Bayilmis C (2012) A blind steganography method based on histograms on video files. Imaging Sci J 60(2):75–82. https://doi.org/10.1179/1743131X11Y.0000000004

    Article  Google Scholar 

  4. Chang PC, Chung KL, Chen JJ, Lin CH, Lin TJ (2014) A DCT/DST-based error propagation-free data hiding algorithm for HEVC intra-coded frames. J Visual Commun Image Representation 25(2):239–253. https://doi.org/10.1016/j.jvcir.2013.10.007

    Article  Google Scholar 

  5. Cheddad A, Condell J, Curran K, Kevitt PM (2008) Enhancing steganography in digital images. Canadian Conf Comput Robot Vision, 326–332. https://doi.org/10.1109/CRV.2008.54

  6. Dasgupta K, Mondal J, Dutta P (2013) Optimized video steganography using genetic algorithm (GA). Procedia Technology 10:131–137. https://doi.org/10.1016/j.protcy.2013.12.345

    Article  Google Scholar 

  7. Ferryman J, Shahrokni A (2009) PETS2009: Dataset And challenge, Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Snowbird, UT, USA, 2009 1–6. https://doi.org/10.1109/PETS-WINTER.2009.5399556

  8. Kar N, Mandal K, Bhattacharya B (2018) Improved chaos-based video steganography using DNA alphabets. ICT Express 4(1):6–13. https://doi.org/10.1016/j.icte.2018.01.003. ISSN 2405-9595

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Mstafa RJ, Elleithy KM (2017) Compressed and raw video steganography techniques: a comprehensive survey and analysis. Multimed Tools Appl 76:21749–21786. https://doi.org/10.1007/s11042-016-4055-1

    Article  Google Scholar 

  11. 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. https://doi.org/10.1109/ACCESS.2017.2691581

    Google Scholar 

  12. Mukherjee S, Sanyal G (2017) Enhanced Position Power First Mapping (PPFM) based Image Steganography. Int J Comput Appl (IJCA), Taylor Francis 39(2):59–68. https://doi.org/10.1080/1206212X.2016.1273624

    Google Scholar 

  13. Mukherjee S, Sanyal G (2017) Enhanced Position Power First Mapping (PPFM) based image steganography. Int J Comput Appl 39(2):59–68. https://doi.org/10.1080/1206212X.2016.1273624

    Google Scholar 

  14. Mukherjee S, Sanyal G (2018) A chaos based image steganographic system. Multimed Tools Appl, Springer 77(21):27851–27876. https://doi.org/10.1007/s11042-018-5996-3

    Article  Google Scholar 

  15. Mukherjee S, Sanyal G (2018) A multi level image steganography methodology based on adaptive PMS and block based pixel swapping. Multimed Tools Appl 78(13):17607–17622. https://doi.org/10.1007/s11042-018-7127-6

    Article  Google Scholar 

  16. Mukherjee S, Sanyal G (2019) Edge based image steganography with variable threshold. Multimed Tools Appl 78:16363–16388. https://doi.org/10.1007/s11042-018-6975-4

    Article  Google Scholar 

  17. Ntalianis K, Doulamis ND, Doulamis AD, Kollias SD (2002) An automatic video-object based steganographic system for multi-use message hiding using wavelet transform. IEEE International Conference on Systems Man and Cybernetics 3:6–12. https://doi.org/10.1109/ICSMC.2002.1176068

    Article  Google Scholar 

  18. Ntalianis K, Tsapatsoulis N (2016) Remote authentication via biometrics: a robust Video-Object steganographic mechanism over wireless networks. IEEE Trans Emerg Topics Comput 4(1):156–174. https://doi.org/10.1109/TETC.2015.2400135

    Article  Google Scholar 

  19. RR, DG, SS (2021) Efficient and secure data hiding in video sequence with three layer security: an approach using chaos. Multimed Tools Appl 80:13865–13878. https://doi.org/10.1007/s11042-020-10324-7

    Article  Google Scholar 

  20. Roy S, Mukherjee S, Sanyal G (2018) Video Steganography Using karhunen-loève Transform. In: Proceedings of the 2nd International Conference on Digital Signal Processing, Tokyo Japan. https://doi.org/10.1145/3193025.3193045, pp 142–146

  21. Roy S, Sanyal G (2016) An approach to selective encryption on compressed image 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, 209-214, https://doi.org/10.1109/IC3I.2016.7917962

  22. Sadek MM, Khalifa AS, Mostafa MGM (2015) Video steganography: A comprehensive review. Multimed Tools Appl 74:7063–7094. https://doi.org/10.1007/s11042-014-1952-z

    Article  Google Scholar 

  23. Sadek MM, Khalifa AS, Mostafa MGM (2017) Robust video steganography algorithm using adaptive skin-tone detection. Multimed Tools Appl 76:3065–3085. https://doi.org/10.1007/s11042-015-3170-8

    Article  Google Scholar 

  24. Schottle P, Böhme R (2016) Game Theory and Adaptive Steganography. IEEE Trans Inform Forens Secur 11(4):760–773. https://doi.org/10.1109/TIFS.2015.2509941

    Article  Google Scholar 

  25. Shahid Z, Chaumont M, Puech W (2013) Considering the reconstruction loop for data hiding of intra- and inter-frames of h.264/AVC. SIViP 7:75–93. https://doi.org/10.1007/s11760-011-0225-9

    Article  Google Scholar 

  26. Song G, Li Z, Zhao J, Hu J, Tu H (2015) A reversible video steganography algorithm for MVC based on motion vector. Multimed Tools Appl 74:3759–3782. https://doi.org/10.1007/s11042-013-1798-9

    Article  Google Scholar 

  27. Sur A, Madhav Krishna SV, Sahu N, Rana S (2015) Detection of motion vector based video steganography. Multimed Tools Appl 74:10479–10494. https://doi.org/10.1007/s11042-014-2181-1

    Article  Google Scholar 

  28. Weng X, Li Y, Chi L, Mu Y (2019) High-Capacity Convolutional video steganography with temporal residual modeling, international conference on multimedia retrieval (ICMR ’19), association for computing machinery, new york, NY, USA, 87–95. https://doi.org/10.1145/3323873.3325011

  29. Wu K, Wang C (2015) Steganography Using Reversible Texture Synthesis. IEEE Trans Image Process 24(1):130–139. https://doi.org/10.1109/TIP.2014.2371246

    Article  MathSciNet  Google Scholar 

  30. Yao Y, Zhang W, Yu N, Zhao X (2015) Defining embedding distortion for motion vector-based video steganography. Multimed Tools Appl 74:11163–11186. https://doi.org/10.1007/s11042-014-2223-8

    Article  Google Scholar 

  31. Zaitoun NM, Musbah J, Aqel MJ (2015) Survey on image segmentation techniques. Procedia Comput Sci 65:797–806. https://doi.org/10.1016/j.procs.2015.09.027

    Article  Google Scholar 

  32. Zhang Y, Zhang M, Yang X, Guo D, Liu L (2017) Novel video steganography algorithm based on secret sharing and error-correcting code for h.264/AVC. Tsinghua Sci Technol 22(2):198–209. https://doi.org/10.23919/TST.2017.7889641

    Article  Google Scholar 

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Roy, S., Howlader, J. & Sanyal, G. A novel approach of data hiding in video using region selection and PCA. Multimed Tools Appl 81, 14553–14571 (2022). https://doi.org/10.1007/s11042-022-12029-5

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