Huffman Encoding with White Tailed Eagle Algorithm-based Image Steganography Technique

Authors

  • Sultan Alkhliwi Assistant Professor Department of Computer Science, Faculty of Science, Northern Border University, Saudi Arabia
Volume: 13 | Issue: 2 | Pages: 10453-10459 | April 2023 | https://doi.org/10.48084/etasr.5501

Abstract

Since the Internet is a medium for transporting sensitive data, the privacy of the message transported has become a major concern. Image steganography has become a prominent tool for hiding data to ensure privacy during transfer. An efficient steganography system is essential to accomplish the best embedding capacity and maintain the other parameters at a satisfying level. Image encryption systems provide a secure and flexible system to maintain the privacy of image conversion and storage in the transmission system. Many existing image steganography methods can be attacked by various techniques, or do not support many image formats for embedding. To resolve these shortcomings, this study presents the Huffman Encoding with White Tailed Eagle Algorithm-based Image Steganography (HEWTEA-IS) technique, aiming to achieve secrecy with no compromise in image quality. The HEWTEA-IS method uses Discrete Wavelet Transform (DWT) for the decomposition of images into different subbands, and Huffman encoding to determine the embedding bits on the decomposed blocks and offer an additional layer of security. Moreover, the WTEA resolves the problem of imperceptibility by identifying the optimal probable position in the cover image for embedding secret bits. The proposed algorithm was simulated and examined in terms of different measures, and an extensive experimental analysis ensured that it is superior to other methods in several aspects.

Keywords:

image steganography, embedding process, Huffman encoding, image decomposition, security

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How to Cite

[1]
Alkhliwi, S. 2023. Huffman Encoding with White Tailed Eagle Algorithm-based Image Steganography Technique. Engineering, Technology & Applied Science Research. 13, 2 (Apr. 2023), 10453–10459. DOI:https://doi.org/10.48084/etasr.5501.

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