ENHANCING IMAGE STEGANOGRAPHY WITH J5 ALGORITHM AND COMPRESSION: A MACHINE LEARNING APPROACH

Abstract

The exponential growth of internet usage for transmitting sensitive information has intensified the demand for advanced security techniques to safeguard digital communication. This study aimed to enhance image steganography by modifying the Least Significant Bit (LSB) method with the J5 algorithm and applying file compression to improve data security and reduce detectability. The method involved embedding hidden text into digital images using a C#-based J5 steganographic tool, secured with password-protected extraction, and evaluating performance with machine learning techniques such as accuracy scoring, confusion matrices, and structural similarity analysis. Results showed that while embedding messages increased pixel modifications and reduced accuracy with larger payloads, the integration of compression reduced inflated stego-image sizes by over 85%, thereby minimizing suspicion without loss of hidden information. Comparative analyses demonstrated that the proposed approach achieved peak signal-to-noise ratio (PSNR) values that were comparable to, and in several cases higher than, those obtained in existing studies, while consistently yielding lower mean squared error (MSE) values across the tested image datasets. In conclusion, this work validates the feasibility of combining steganography, machine learning, and compression to achieve more practical, secure, and efficient data hiding in modern communication systems.