Journal of Mathematics and Statistics

Randomized Pixel Selection for Enhancing LSB Algorithm Security against Brute-Force Attack

Ammar Y. Tuama, Mohamad A. Mohamed, Abdullah Muhammed and Zurina M. Hanapi

DOI : 10.3844/jmssp.2017.127.138

Journal of Mathematics and Statistics

Volume 13, Issue 2

Pages 127-138

Abstract

Steganography is the science of concealing a secret message by embedding it into innocent carriers such as text, audio, images, etc. It plays a crucial role in a broad range of security applications such as securing message exchange, user authentication and copyrighting. One of the most effortless and widely-used techniques is the age-old Least Significant Bits (LSB) algorithm, which can be implemented in both transformative and spatial domains. The advantage of this technique is that it can be used with any form of digital media. However, operating pixels on a sequential basis leaves the algorithm susceptible to many steganalysis techniques. Consequently, it is easy for the attacker to recognize the inclusion of a secret message within the media and thus to proceed with the extraction. Therefore, it is necessary to provide an extra layer of security to protect the data. In this study, we propose a random selection of pixels that hold a secret message based on an integer solution of the elliptic curve equation. In addition, we have embedded noise bits into the unused pixels to make the steganalysis process more difficult. The attacker not only needs to guess which pixels (out of all pixels in the image) have been selected to carry the secret, but also must arrange them in the correct order. The results show that the proposed algorithm achieves a significant security improvement in comparison to standard LSB when it comes to defending against brute-force attacks with a subordinate effect of image quality.

Copyright

© 2017 Ammar Y. Tuama, Mohamad A. Mohamed, Abdullah Muhammed and Zurina M. Hanapi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.