An Enhancement of Data Hiding Imperceptibility using Slantlet Transform (SLT)
Corresponding Author(s) : Daurat Sinaga
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol 4, No 1, February 2019
Abstract
This study proposes a hybrid technique in securing image data that will be applied in telemedicine in future. Based on the web-based ENT diagnosis system using Virtual Hospital Server (VHS), patients are able to submit their physiological signals and multimedia data through the internet. In telemedicine system, image data need more secure to protect data patients in web. Cryptography and steganography are techniques that can be used to secure image data implementation. In this study, steganography method has been applied using hybrid between Discrete Cosine Transform (DCT) and Slantlet Transform (SLT) technique. DCT is calculated on blocks of independent pixels, a coding error causes discontinuity between blocks resulting in annoying blocking artifact. While SLT applies on entire image and offers better energy compaction compare to DCT without any blocking artifact. Furthermore, SLT splits component into numerous frequency bands called sub bands or octave bands. It is known that SLT is a better than DWT based scheme and better time localization. Weakness of DCT is eliminated by SLT that employ an improved version of the usual Discrete Wavelet Transform (DWT). Some comparison of technique is included in this study to show the capability of the hybrid SLT and DCT. Experimental results show that optimum imperceptibility is achieved.
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- C. Kuo and J.-J. Liu, “Development of a web-based telemedicien system for remote ENT diagnoses,” in 2010 International Conference on System Science and Engineering, 2010, pp. 565–570.
- A. a. Shejul and U. L. Kulkarni, “A DWT Based Approach for Steganography Using Biometrics,” in 2010 International Conference on Data Storage and Data Engineering, 2010, pp. 39–43.
- I. W. Selesnick, “The slantlet transform,” IEEE Trans. Signal Process., vol. 47, no. 5, pp. 1304–1313, May 1999.
- S. Kumar and S. K. Muttoo, “Distortionless Data Hiding Based on Slantlet Transform,” in 2009 International Conference on Multimedia Information Networking and Security, 2009, pp. 48–52.
- M. Maitra, A. Chatterjee, and F. Matsuno, “A novel scheme for feature extraction and classification of magnetic resonance brain images based on Slantlet Transform and Support Vector Machine,” in 2008 SICE Annual Conference, 2008, pp. 1130–1134.
- A. Chatterjee, M. Maitra, and S. K. Goswami, “Classification of overcurrent and inrush current for power system reliability using Slantlet transform and artificial neural network,” Expert Syst. Appl., vol. 36, no. 2, pp. 2391–2399, Mar. 2009.
- S. Kumar and S. K. Muttoo, “Steganography based on Contourlet Transform,” Int. J. Comput. Sci., vol. 9, no. 6, pp. 215–220, 2011.
- S. K. Mutt and S. Kumar, “Secure image Steganography based on Slantlet transform,” in Methods and Models in Computer Science, 2009.
- S. Shrestha and K. Wahid, “Hybrid DWT-DCT algorithm for biomedical image and video compression applications,” in 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010), 2010, no. Isspa, pp. 280–283.
References
C. Kuo and J.-J. Liu, “Development of a web-based telemedicien system for remote ENT diagnoses,” in 2010 International Conference on System Science and Engineering, 2010, pp. 565–570.
A. a. Shejul and U. L. Kulkarni, “A DWT Based Approach for Steganography Using Biometrics,” in 2010 International Conference on Data Storage and Data Engineering, 2010, pp. 39–43.
I. W. Selesnick, “The slantlet transform,” IEEE Trans. Signal Process., vol. 47, no. 5, pp. 1304–1313, May 1999.
S. Kumar and S. K. Muttoo, “Distortionless Data Hiding Based on Slantlet Transform,” in 2009 International Conference on Multimedia Information Networking and Security, 2009, pp. 48–52.
M. Maitra, A. Chatterjee, and F. Matsuno, “A novel scheme for feature extraction and classification of magnetic resonance brain images based on Slantlet Transform and Support Vector Machine,” in 2008 SICE Annual Conference, 2008, pp. 1130–1134.
A. Chatterjee, M. Maitra, and S. K. Goswami, “Classification of overcurrent and inrush current for power system reliability using Slantlet transform and artificial neural network,” Expert Syst. Appl., vol. 36, no. 2, pp. 2391–2399, Mar. 2009.
S. Kumar and S. K. Muttoo, “Steganography based on Contourlet Transform,” Int. J. Comput. Sci., vol. 9, no. 6, pp. 215–220, 2011.
S. K. Mutt and S. Kumar, “Secure image Steganography based on Slantlet transform,” in Methods and Models in Computer Science, 2009.
S. Shrestha and K. Wahid, “Hybrid DWT-DCT algorithm for biomedical image and video compression applications,” in 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010), 2010, no. Isspa, pp. 280–283.