Secure Evaluation of Image Steganalysis Based on Machine Learning Algorithms - Rashid, Dlsoz Abdalkarim; Mohammed, Marwan B.; Mohammed, Iqbal A. Baki; Attar, Tara Nawzad Ahmad Al (Sulaimani Journal for Pure and Applied Sciences, 2024)
The art of discovering concealed messages submerged in digital media using steganography in a secured form is known as steganalysis. Steganography and steganalysis have both gotten a lot of significance from law enforcement agencies and the media. More specifically, universal steganalysis techniques have grown in admiration since they perform regardless of the embedding technique. This paper comparing between three machine learning (ML) algorithms: Support Vector Machine (SVM), Naive Bays (NB), and KNearest Neighbor (KNN). These algorithms detect and classify whether images have embedded data within them or not; the images used in the data set have been referred to as “cover images” which means no data is embedded or “stego images” which means data had been submerged. The experiment in this study was run on a genuine dataset of categorized images. Each image in this dataset contains two embedding rates: half embedding (HE) and full embedding (FE). As a result, the comparison of the aforementioned algorithms evaluations showed that SVM is more accurate than NB and KNN algorithms when applied to HE images data, with an accuracy of 0.88 for the 50% test sample and 0.90 for the 30% test sample. When the test sample 50% was 0.82 and the sample 30% was 0.86 the SVM algorithm outperformed also. The development of more accurate and effective techniques using machine learning algorithms with help in the improvement of the security of digital communication which in turn will prevent the unauthorized transfer of sensitive information.
Keywords: Cover image; Full Embedded Message and Half Embedded Message; Image Steg analysis; Stego image; Steganography