Steganalysis of Image Steganography using Machine Learning

This study explores the crucial area of steganalysis with an emphasis on uncovering hidden information in digital photos. The art of data concealment, or steganography, presents a serious security risk and calls for strong steganalysis methods. Using a hybrid machine learning approach, this study combines the advantages of Random Forests, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs).The ability of CNNs to extract spatial characteristics from images is essential for spotting minute changes brought about by steganographic techniques. An ensemble learning method called Random Forests offers reliable categorization using a variety of feature sets. Patterns in pixel dependencies are analyzed by LSTM networks, which are built for sequential data and may show temporal anomalies brought about by embedding procedures. To improve the detection of image steganography, the complementary strengths of these three different machine learning algorithms are successfully leveraged by choosing the result from the model that exhibits the highest accuracy as the final output.