Skip to main content
Log in

Maximizing embedding capacity and stego quality: curve-fitting in the transform domain

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

Abstract

Achieving high embedding capacities for information hiding systems while maintaining high perceptual stego quality is a critical challenge in steganography. This quandary is attracting researchers to overcome the trade-off barrier between high capacities and enhanced levels of stego image quality. This work introduces a promising transform-domain hiding scheme that aims to achieve ultimate hiding capacity with premium perceptual quality results. The proposed scheme is based on the fact that highly correlated images are represented by significant coefficients that are strongly packed in the transform-domain of the image. This allows for a large space in the insignificant coefficient areas to embed in. To exploit this feature optimally, a curve-fitting approach is introduced and implemented in various adaptive-region transform-domain embedding schemes. Experimental results demonstrate that this curve-fitting methodology is able to enhance adaptive transform-domain embedding schemes where very high embedding capacities can be achieved that are much higher than competing high-capacity hiding schemes. The other noticeable result is that although the embedding capacity has increased compared to earlier work, the perceptual quality level has also improved over previous 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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Ahmed N, Natarajan T, Rao K (1974) Discrete cosine transform. IEEE Trans Comput 23(1):90–93

    Article  MathSciNet  MATH  Google Scholar 

  2. Anderson RJ, Petitcolas FA (1998) On the limits of steganography. IEEE J Sel Areas Commun 16(4):474–481

    Article  Google Scholar 

  3. Bracamonte J, Ansorge M, Pellandini F, Farine PA (2000) Low complexity image matching in the compressed domain by using the dct-phase Proc. of the 6th COST, vol 276, pp 88–93

  4. Bracamonte J, Ansorge M, Pellandini F, Farine PA (2005) Efficient compressed domain target image search and retrieval Image and video retrieval. Springer, pp 154–163

    Google Scholar 

  5. Brisbane G, aini RSN, Ogunbona P (2005) High-capacity steganography using a shared colour palette. IEE Proc Vis Image Signal Process 152(6):787–792

    Article  Google Scholar 

  6. Chan CK, Cheng L (2004) Hiding data in images by simple LSB substitution. Pattern Recogn 37:469–474

    Article  MATH  Google Scholar 

  7. Chang CC, Chen TS, Chung LZ (2002) A steganographic method based upon jpeg and quantization table modification. Inf Sci 141(1):123–138

    Article  MATH  Google Scholar 

  8. Chen B, Wornell G (2001) Quantization index modulation: a class of provably good methods for digital watermarking and information embedding. IEEE Trans Information Theory 47(4):1423–1443

    Article  MathSciNet  MATH  Google Scholar 

  9. Cole E (2003) Hiding in plain sight: Steganography and the art of Covert communication, 1st edn. Wiley, New York

    Google Scholar 

  10. Ebrahimpour-Komleh H, Chandran V, Sridharan S (2001) Face recognition using fractal codes Proceedings of the 2001 international conference on image processing, 2001, vol 3, pp 58–61

    Google Scholar 

  11. El Safy R, Zayed H, El Dessouki A (2009) An adaptive steganographic technique based on integer wavelet transform International conference on networking and media convergence, 2009. ICNM 2009, pp 111–117

    Google Scholar 

  12. Ibaida A, Khalil I (2013) Wavelet-based ecg steganography for protecting patient confidential information in point-of-care systems. IEEE Trans Biomed Eng 60 (12):3322–3330

    Article  Google Scholar 

  13. IEC I (1994) Information technology-digital compression and coding of continuous-tone still images: Requirements and guidelines. Standard, ISO IEC pp. 10,918–1

  14. Jain A, Uludag U, Hsu R (2002) Hiding a face in a fingerprint image Proc of the international conference on pattern recognition (ICPR). Quebec city, Canada

    Google Scholar 

  15. Lee Y, Chen L (2000) High capacity image steganographic model. IEE Proc, Vis Image Signal Process 147(3):288–294

    Article  Google Scholar 

  16. Lee YK, Chen LH (2000) High capacity image steganographic model. IEE Proceedings-Vision Image and Signal Process 147(3):288–294

    Article  Google Scholar 

  17. Leng CK, Labadin J, Juan SFS (2008) Steganography: Dct coefficients reparation technique in jpeg image. JDCTA 2(2):35–41

    Google Scholar 

  18. Lin CC, Shiu PF (2010) High capacity data hiding scheme for dct-based images. J Infor Hiding and Multimed Signal Process 1(3):220–240

    Google Scholar 

  19. Lu P, Luo X, Tang Q, Shen L (2004) An improved sample pairs method for detection of lsb embedding International workshop on information hiding. Springer, pp 116–127

  20. Marvel LM, Charles G, Boncelet J, Retter CT (1999) Spread spectrum image steganography. IEEE Trans Image Process 8(8):1075–1083

  21. Nozaki K, Niimi M, Eason RO, Kawaguchi E (1998) A large capacity steganography using color bmp images ACCV ’98: Proceedings of the third asian conference on computer vision-Volume I. Springer, London, pp 112–119

    Google Scholar 

  22. Pavlidis G, Tsompanopoulos A, Papamarkos N, Chamzas C (2003) Jpeg2000 over noisy communication channels thorough evaluation and cost analysis. Signal Process Image Commun 18(6):497–514

    Article  Google Scholar 

  23. Qazanfari K, Safabakhsh R (2013) High-capacity method for hiding data in the discrete cosine transform domain. J Electron Imaging 22(4):043,009–043,009

    Article  Google Scholar 

  24. Qin C, Chang CC, Chiu YP (2014) A novel joint data-hiding and compression scheme based on SMVQ and image inpainting. IEEE Trans Image Process 23(3):969–978

    Article  MathSciNet  MATH  Google Scholar 

  25. Qin C, Chang CC, Hsu TJ (2015) Reversible data hiding scheme based on exploiting modification direction with two steganographic images. Multimed Tool Appl 74(15):5861–5872

    Article  Google Scholar 

  26. Qin C, Chang CC, Huang YH, Liao LT (2013) An inpainting-assisted reversible steganographic scheme using a histogram shifting mechanism. IEEE Trans Circ Syst Video Technol 23(7):1109–1118

    Article  Google Scholar 

  27. Qin C, Zhang X (2015) Effective reversible data hiding in encrypted image with privacy protection for image content. J Vis Commun Image Represent 31:154–164

    Article  Google Scholar 

  28. Rabie T (2007) Frequency-domain data hiding based on the matryoshka principle. Special Issue on Advances in Video Processing and Security Analysis for Multimedia Communications. Int J Advan Media Commun 1(3):298–312

    Article  Google Scholar 

  29. Rabie T (2010) Data secrecy: An FFT approach. Advanced Techniques in Multimedia Watermarking: Image, Video and Audio Applications pp 21–35. doi:10.4018/978-1-61520-903-3.ch002

  30. Rabie T (2013) High-capacity steganography 6th international congress on image and signal processing (CISP), vol 2, pp 858–863

  31. Rabie T (2016) Color-secure digital image compression. Multimedia Tools and Applications. doi:10.1007/s11,042--016--3942--9

  32. Rabie T, Kamel I (2015) On the embedding limits of the discrete cosine transform. Multimedia Tools and Applications 75(10). doi:10.1007/s11,042--015--2557--x

  33. Rabie T, Kamel I (2016) High-capacity steganography: A global-adaptive-region discrete cosine transform approach. Multimedia Tools and Applications 76(5). doi:10.1007/s11,042--016--3301--x

  34. Rabie T, Kamel I (2016) Toward optimal embedding capacity for transform domain steganography: A quad-tree adaptive-region approach. Multimedia Tools and Applications 76(6). doi:10.1007/s11,042--016--3501--4

  35. Raja K, Chowdary C, Venugopal K, Patnaik L (2005) A secure image steganography using lsb, dct and compression techniques on raw images 2005 3rd international conference on intelligent sensing and information processing, pp 170–176

    Chapter  Google Scholar 

  36. Raja K, Venugopal K, Patnaik L, et al (2006) High capacity lossless secure image steganography using wavelets 2006 International conference on advanced computing and communications, pp 230–235

    Chapter  Google Scholar 

  37. Rao K, Yip P (1990) Discrete cosine transform: algorithms, Advantages, Applications. Academic Press, ISBN 0-12-580203-X Boston

  38. Rodrigues J, Rios J, Puech W, et al (2004) Ssb-4 system of steganography using bit 4 5Th international workshop on image analysis for multimedia interactive services

    Google Scholar 

  39. Roque JJ, Minguet JM (2009) Slsb: Improving the steganographic algorithm lsb WOSIS, pp 57–66

    Google Scholar 

  40. Solanki K, Jacobsen N, Madhow U, Manjunath BS, Chandrasekaran S (2004) Robust image-adaptive data hiding using erasure and error correction. IEEE Trans Image Process 13(12):1627–1639

    Article  Google Scholar 

  41. Sumathi C, Santanam T, Umamaheswari G (2014) A study of various steganographic techniques used for information hiding. arXiv:1401.5561

  42. Wang Z, Bovik AC (2009) Mean squared error: love it or leave it? A new look at signal fidelity measures. IEEE Signal Process Mag 26(1):98–117

    Article  Google Scholar 

  43. 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

    Article  Google Scholar 

  44. Yang B, Schmucker M, Funk W, Busch C, Sun S (2004) Integer dct-based reversible watermarking for images using companding technique. Proc. SPIE 5306, Security, Steganography and Watermarking of Multimedia Contents 6(405)

Download references

Acknowledgments

The authors would like to thank the five anonymous reviewers for their valuable suggestions that helped improve the original manuscript. This work was supported by the College of Graduate Studies and Research at the University of Sharjah.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tamer Rabie.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rabie, T., Kamel, I. & Baziyad, M. Maximizing embedding capacity and stego quality: curve-fitting in the transform domain. Multimed Tools Appl 77, 8295–8326 (2018). https://doi.org/10.1007/s11042-017-4727-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4727-5

Keywords

Navigation