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Computationally efficient joint imperceptible image watermarking and JPEG compression: a green computing approach


This paper presents a computationally efficient joint imperceptible image watermarking and joint photographic experts group (JPEG) compression scheme. In recent times, the transmission and storage of digital documents/information over the unsecured channel are enormous concerns and nearly all of the digital documents are compressed before they are stored or transmitted to save the bandwidth requirements. There are many similar computational operations performed during watermarking and compression which lead to computational redundancy and time delay. This demands development of joint watermarking and compression scheme for various multimedia contents. In this paper, we propose a technique for image watermarking during JPEG compression to address the optimal trade-off between major performance parameters including embedding and compression rates, robustness and embedding alterations against different known signal processing attacks. The performance of the proposed technique is extensively evaluated in the form of peak signal to noise ratio (PSNR), correlation, compression ratio and execution time for different discrete cosine transform (DCT) blocks and watermark sizes. Embedding is done on DCT coefficients using additive watermarking.

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  1. 1.

    Badshah G, Liew S-C (2016) J M Zain and M Ali, watermark compression in medical image watermarking using Lempel-Ziv-Welch (LZW) lossless compression technique. J Digit Imaging 29(2):216–225

    Article  Google Scholar 

  2. 2.

    Goudiaa D, Chaumont M, Puech W, Said NH (2011) A joint JPEG2000 compression and watermarking system using a TCQ-based quantization scheme, in: visual information processing and communication II(VIPC 2011), 78820C–78820C

  3. 3.

    Guillemot L and Moureaux J (2006) Indexing lattice vectors in a joint watermarking and compression scheme, vol 2. In: IEEE Int. Conf. Acoustics, Speech. Signal Processing, Toulouse, pp 329–332

  4. 4.

    Guo J-M, Liu Y-F (2010) Joint compression/watermarking scheme using majority parity guidance and Halftoning-based block truncation coding. IEEE Trans Image Process 19(8):2056–2069

    MathSciNet  Article  MATH  Google Scholar 

  5. 5.

    Javeed A, Singh AK (2016) Robust and imperceptible image watermarking in DWT-BTC domain. Int J Electro Sec Digit Foren Inder Sci 8(1):53–62

    Google Scholar 

  6. 6.

    Lin MH, Chang CC (2004) A novel information hiding scheme based on BTC. In: Proc Int Conf Computer and Information Technology, Wuhan, pp 66–71

  7. 7.

    Lin SD, Shie S-C, Guo JY (2010) Improving the robustness of DCT-based image watermarking against JPEG compression. J Comp Stand Inter 32(1–2):54–60

    Article  Google Scholar 

  8. 8.

    Maheshawari JP, Kumar M, Mathur Garima, Yadav RP, Kakerda RK (2015) Robust digital image watermarking using DCT based pyramid transform via image compression. In: Proc of 2015 International Conference on Communications and Signal Processing, Melmaruvathur, pp 1059–1063

  9. 9.

    Mary SJJ, Christopher CS, Joe SSA (2016) Novel scheme for compressed image authentication using LSB watermarking and EMRC6 encryption. Circuits Syst 7(8):1722–1733

  10. 10.

    Pereira S, Voloshynovskiy S, Madueño M, Marchand-Maillet S, Pun T (2001) Second generation benchmarking and application oriented evaluation. Information hiding workshop III, Pittsburgh, In, pp 340–353

    MATH  Google Scholar 

  11. 11.

    Qureshi S, Nair S (2013) LSB Based Image Watermarking with Hybrid Compression-Encryption Technique. Advance EngTechnol Series 6:161–165

    Google Scholar 

  12. 12.

    Singh AK, Kumar B, Singh G, Mohan A (2017) Medical image watermarking: techniques and applications, book series on multimedia systems and applications. Springer, USA

  13. 13.

    Singh AK, Kumar B, Dave M, Ghrera SP, Mohan A (2016) Digital image watermarking: techniques and emerging applications. Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security. IGI Global, USA, pp 246–272.

  14. 14.

    Tian J (2002) Wavelet based reversible watermarking for authentication. In: Proceedings of SPIE security watermarking multimedia contents IV. CA, San Jose, pp 679–690

    Chapter  Google Scholar 

  15. 15.

    Xie L, Arce GR (1998) Joint wavelet compression and authentication watermarking, In Proceedings of the 5th IEEE International Conference on Image Processing

  16. 16.

    Zear A, Singh AK, Kumar P (2016) A proposed secure multiple watermarking technique based on DWT, DCT and SVD for application in medicine. Multimedia Tools and Applications.

  17. 17.

    Zhou Y (2010) Joint robust watermarking and image compression, IEEE International Workshop on Information Forensics and Security, 12-15 Dec

  18. 18.

    Zhou Y and Yang E-H (2009) Joint robust watermarking and compression using variable-rate scalar quantization in proc. of The 11th Canadian Workshop on Information Theory, Ottawa, Canada, May

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Correspondence to Amit Kumar Singh.

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Srivastava, R., Kumar, B., Singh, A.K. et al. Computationally efficient joint imperceptible image watermarking and JPEG compression: a green computing approach. Multimed Tools Appl 77, 16447–16459 (2018).

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  • Watermarking
  • Compression
  • Jpeg
  • DCT
  • Quantization
  • Checkmark attacks