AUTOMATION DETECTION OF STEGANOGRAPHICAL CONTENT USING MACHINE LEARNING

Abstract: The rapid growth of digital communication has significantly increased the use of steganography for secure and covert information exchange. While steganography serves legitimate privacy-preserving purposes, it is also widely exploited for unauthorized communication, cybercrime, and data exfiltration. This research paper presents a novel machine learning–based framework for the automated detection of steganographical content in digital images. The proposed system uses feature extraction, statistical image analysis, and supervised learning techniques to identify hidden data embedded through spatial and frequency-domain steganographic methods.

The study focuses on commonly used embedding techniques such as Least Significant Bit (LSB), transform-domain hiding, and adaptive image embedding. Machine learning classifiers including Support Vector Machine (SVM), Random Forest, Convolutional Neural Network (CNN), and Gradient Boosting are evaluated to improve steganalysis accuracy. Experimental results demonstrate that the CNN-based model achieves superior detection performance in terms of accuracy, precision, recall, F1-score, and robustness against noise and compression.

This work contributes to the field of cybersecurity and digital forensics by providing an intelligent, scalable, and automated solution for detecting concealed information in multimedia files.

Keywords: Steganography, Steganalysis, Machine Learning, CNN, Image Forensics, Cybersecurity, Hidden Data Detection