Open Access
ENHANCED IMAGE STEGANOGRAPHY: LSB SUBSTITUTION WITH RUN-LENGTH ENCODED SECRET DATA
4
Department of Computer Science, University of São Paulo, Brazil
Abstract
Image steganography has emerged as a vital technique for secure communication by concealing sensitive information within innocuous digital media. This study proposes an enhanced image steganography method that integrates Least Significant Bit (LSB) substitution with run-length encoding (RLE) of the secret data to improve embedding efficiency and reduce detectability. By applying run-length encoding prior to embedding, the secret message is compressed, enabling a greater volume of information to be hidden within the cover image while maintaining minimal perceptual distortion. The proposed approach adaptively selects embedding regions based on local image characteristics to further increase imperceptibility and robustness against steganalysis. Experimental results demonstrate that the method achieves higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to conventional LSB substitution techniques without compression. This research highlights the potential of combining data compression and adaptive embedding strategies to advance the state of image steganography, offering a practical solution for secure data hiding in modern digital communication environments.
Keywords
Image steganography,
LSB substitution,
Run-length encoding,
Data hiding
References
Kohei Arai. (2000). Fundamental Theory on Wavelet Analysis. Morikita Shuppan Publishing Co. Ltd.
Kohei Arai. (2006). Self Learning on Wavelet Analysis. Kindai-Kagakusha Publishing Co. Ltd.
Kohei Arai & Kaname Seto. (2002). Data hiding method based on MultiResolution Analysis (MRA). Visualization Society of Japan, 22(Suppl. No.1), 229–232.
Kohei Arai & Kaname Seto. (2005). Data hiding method with coordinate conversion in feature space. Visualization Society of Japan, 25(Suppl. No.1), 55–58.
Kohei Arai & Kaname Seto. (2007). Improvement of invisibility of secret images embedded in circulate images based on MRA with coordinate conversion and Principal Component Analysis (PCA). Journal of Image and Electronics Society of Japan, 36(5), 665–673.
Kohei Arai & Kaname Seto. (2009). Improvement of invisibility of secret images embedded in circulate images based on MRA with scanning scheme conversion. Visualization Society of Japan, 29(Suppl. No.1), 167–170.
Kohei Arai. (2010). Improvement of security and invisibility of secret images embedded in circulate images based on MRA. Report of RIMS - Research Institute for Mathematical Sciences Kyoto University, ISSN188-2818, No.1684, 93–113.
Mallat, S., & Zhong, S. (1992). Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 710–732.
Kohei Arai. (2013). Method for data hiding based on Legall 5/2 (Cohen-Daubechies-Feauveau: CDF 5/3) wavelet with data compression and random scanning of secret imagery data. International Journal of Wavelets Multiresolution and Information Processing, 11(4), Article B60006, 1–18. DOI:10.1142/S0219691313600060.
Kohei Arai & Yuji Yamada. (2012). Improvement of secret image invisibility in distribute image with dyadic wavelet based data hiding with run-length coded secret images of which location of codes are determined with random number. International Journal of Advanced Research in Artificial Intelligence (IJARAI), Special Issue on Artificial Intelligence, 33–40.
Vision Lab, Kyoto University. (2011). Image Database. Retrieved March 11, 2011, from http://vision.kuee.kyotou.ac.jp/IUE/IMAGE_DATABASE/STD_IMAGES/
Kohei Arai & Leland Jameson. (2001). Earth Observation Satellite Data Analysis Based on Wavelet Analysis. Morikita-Shuppan Publishing Co. Ltd.
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