ENHANCED IMAGE STEGANOGRAPHY: LSB SUBSTITUTION WITH RUN-LENGTH ENCODED SECRET DATA
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
References
Similar Articles
- Mateo Laurent Dufour, Architecting Secure and Scalable Production Machine Learning Systems: Integrating Model Management, High Performance Computing, and Cloud Native Infrastructure , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 03 (2026): Volume 03 Issue 03
- Dr. Elena Petrova, Prof. David J. Hernandez, MACHINE LEARNING MODEL IMPLEMENTATION STRATEGIES AND PREDICTIVE FACTORS FOR PREECLAMPSIA FORECASTING: A REVIEW , International Journal of Intelligent Data and Machine Learning: Vol. 1 No. 01 (2024): Volume 01 Issue 01
- Ananya Patel (Ph.D. Candidate), ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 02 (2025): Volume 02 Issue 02
- Kartik Tandon, Dr. Priya Menon, LEVERAGING MACHINE LEARNING TO IDENTIFY MATERNAL RISK FACTORS FOR CONGENITAL HEART DISEASE IN OFFSPRING , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 05 (2025): Volume 02 Issue 05
- Dr. Amir Reza Khosravi, Distributed Stream Processing Models for Financial Markets: A Theoretical Investigation of Kafka-Based Infrastructure in High-Frequency Digital Finance Systems , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 06 (2026): Volume 03 Issue 06
- Dr. Larian D. Venorth, Prof. Maevis K. Durand, The Transformative Trajectory Of Large Language Models: Societal Impact, Predictive Limitations, And The Unforeseen Geohazard Nexus , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Dr. Kwame Mensah, Dr. Abena Owusu, An Interpretable Visual Analytics Framework for Machine Learning–Based Multichannel Time Series Classification and Performance Evaluation , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Prof. Kai O. Chen, DEVELOPING AND VALIDATING A COMPREHENSIVE DISCOURSE ANNOTATION GUIDELINE FOR LOW-RESOURCE LANGUAGES , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Dr. Emil Novak, Deep Learning For E‑Commerce Recommendations: Capturing Long- And Short-Term User Preferences With Cnn-Based Representation Learning , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 12 (2025): Volume 02 Issue 12
- Igor Litovsky, A Systematic Review of Machine Learning Approaches For AI-Driven Fraud Detection in Loyalty Programs , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
You may also start an advanced similarity search for this article.