Ways to Increase the Efficiency of Steganographic Use of Fractal Image Compression Algorithm

Mohammed Sardar Ali; Noura Qusay Ebraheem

doi:10.25212/lfu.qzj.6.4.38

Authors

Mohammed Sardar Ali Department of Information Technology, College Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq
Noura Qusay Ebraheem Department of Information Technology, College Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq

DOI:

https://doi.org/10.25212/lfu.qzj.6.4.38

Keywords:

image processing, lossless compression, digital image compression, stego algorithm, steganography.

Abstract

The state of the art in fractal picture compression algorithms is investigated. The primary directions for improving compression algorithms are discussed. In terms of classification efficiency and picture processing speed, the methods are among the most effective. The concept of steganographic use of the fractal algorithm is discussed. The differences between the advanced compression algorithm and the classic compression algorithm are evaluated to accomplish so. The differences discovered are used to define ways to improve the efficiency of the stego algorithm. The development of digital image compression technology was spurred by the necessity for speedy communication and "live" digital image information over the internet. Time has passed, and many tactics exist now to reduce the compression ratio and increase the usability of speedy computation, but because we are limited by certain constraints, there are many inventive ways to overcome these limitations. Today's environment is heavily reliant on digital media storage, necessitating the creation of more effective image or data compression algorithms. Images must be compressed and soft-encoded before being used in the transmission phase due to limited bandwidth and power. This paper discusses the differences between Lossy and Lossless compression methods as they apply to image processing.

Downloads

Download data is not yet available.

References

Altaay, A. A. J., Sahib, S. B., & Zamani, M. (2012, November). An introduction to image steganography techniques. In 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT) (pp. 122-126). IEEE.

Al-Yousuf, F. Q. A., & Din, R. (2020). Review on secured data capabilities of cryptography, steganography, and watermarking domain. Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 17(2), 1053-1059.

Balaji, B. S., Rajkumar, R. S., & Ibrahim, B. F. (2019). Service profile based ontological system for selection and ranking of business process web services. International Journal of Advanced Trends in Computer Science and Engineering, 8, 18-22.

Cierniak, R., & Rutkowski, L. (2000). On image compression by competitive neural networks and optimal linear predictors. Signal Processing: Image Communication, 15(6), 559-565.

Deshlahra, A. (2013). Analysis of Image Compression Methods Based On Transform and Fractal Coding (Doctoral dissertation)..

Dhawan, S. (2011). A review of image compression and comparison of its algorithms. International Journal of electronics & Communication technology, 2(1), 22-26.

Din, R., & Qasim, A. J. (2019). Steganography analysis techniques applied to audio and image files. Bulletin of Electrical Engineering and Informatics, 8(4), 1297-1302.

Din, R., Ghazali, O., & Qasim, A. J. (2018). Analytical Review on Graphical Formats Used in Image Steganographic Compression. Indonesian Journal of Electrical Engineering and Computer Science, 12(2), 441-446.

Din, R., Mahmuddin, M., & Qasim, A. J. (2019). Review on steganography methods in multi-media domain. International Journal of Engineering & Technology, 8(1.7), 288-292.

Hmood, A. K., Jalab, H. A., Kasirun, Z. M., Zaidan, A. A., & Zaidan, B. B. (2010). On the capacity and security of steganography approaches: An overview. Journal of Applied Sciences, 10(16), 1825-1833.

Holub, V., & Fridrich, J. (2013, June). Digital image steganography using universal distortion. In Proceedings of the first ACM workshop on Information hiding and multimedia security (pp. 59-68).

Jain, A. K., & Uludag, U. (2002, March). Hiding fingerprint minutiae in images. In Proceedings of 3rd Workshop on Automatic Identification Advanced Technologies (pp. 97-102).

Kh, T., & Hamarash, I. (2020). Model-Based Quality Assessment of Internet of Things Software Applications: A Systematic Mapping Study.

Khare, A., Kunari, M., & Khare, P. (2010). Efficient algorithm for digital image steganography. Journal of Information Science, Knowledge and Research in Computer Science and Application, 1-5.

Mainberger, M., Schmaltz, C., Berg, M., Weickert, J., & Backes, M. (2012, July). Diffusion-based image compression in steganography. In International Symposium on Visual Computing (pp. 219-228). Springer, Berlin, Heidelberg.

Memon, Q. A. (2014). Embedding Authentication and Distortion Concealment in Images–A Noisy Channel Perspective. Proceeding of the Electrical Engineering Computer Science and Informatics, 1(1), 385-390.

Miran, A., & Kadir, G. (2019). Enhancing AODV routing protocol to support QoS. International Journal of Advanced Trends in Computer Science and Engineering, 8(5), 1824–1830.

Mohammed, A. G. (2021). A Study of Scheduling Algorithms in LTE-Advanced HetNet. QALAAI ZANIST SCIENTIFIC JOURNAL, 6(3), 945-968.

Niu, H., Shang, Y., Yang, X., Xu, D., Han, B., & Chen, C. (2010, June). Design and research on the JPEG-LS image compression algorithm. In 2010 Second International Conference on Communication Systems, Networks and Applications (Vol. 1, pp. 232-234). IEEE.

Qasim, A. J., Din, R., & Alyousuf, F. Q. A. (2020). Review on techniques and file formats of image compression. Bulletin of Electrical Engineering and Informatics, 9(2), 602-610..

QASSIM, A. J., & Sudhakar, Y. (2015). Information Security with Image through Reversible Room by using Advanced Encryption Standard and Least Significant Bit Algorithm. International Journal of Advances in Computer Science and Technology, 4(4), 93-97.

Saroya, N., & Kaur, P. (2014). Analysis of image compression algorithm using DCT and DWT transforms. International Journal of Advanced Research in Computer Science and Software Engineering, 4(2), 897-900.

Semenov, S., Voloshyn, D., & Ahmed, A. N. (2019). Mathematical model of the implementation process of flight task of unmanned aerial vehicle in the conditions of external impact. International Journal of Advanced Trends in Computer Science and Engineering, 8(1), 7-13.

Tayel, M., Shawky, H., & Hafez, A. E. D. S. (2012, February). A new chaos steganography algorithm for hiding multimedia data. In 2012 14th International Conference on Advanced Communication Technology (ICACT) (pp. 208-212). IEEE.

Tummala, S. V., & Marni, V. (2017). Comparison of Image Compression and Enhancement Techniques for Image Quality in Medical Images..

Valenzise, G., Tagliasacchi, M., Tubaro, S., Cancelli, G., & Barni, M. (2009, November). A compressive-sensing based watermarking scheme for sparse image tampering identification. In 2009 16th IEEE International Conference on Image Processing (ICIP) (pp. 1265-1268). IEEE.

Vasyura AS, Z. E., Lukichov VV (2006). Adaptive method of embedding data in fractal image code Information technologies and computer engineering, № 2 (6), P105- 112.

Zaidan, B. B., Zaidan, A. A., Taqa, A., & Othman, F. (2009). Stego-image vs stego-analysis system. International Journal of Computer and Electrical Engineering, 1(5), 572.

Zeki, A. M., Ibrahim, A. A., & Manaf, A. A. (2012). Steganographic software: analysis and implementation. International Journal of Computers and Communications, 6(1), 35-42.