Steganalysis on Medical Images with Support Vector Machine

DICOM is a file format standard for medical images. DICOM file format includes a header with the patient's personal information as well as information related to the image. In steganography applications on medical data, patient's personal information, reports, and other data are hidden in the medical images. Steganalysis detects whether any secret data is hidden in a file or not. In this study, a steganalysis method is proposed to detect the presence of hidden data in a medical image. For this reason, the secret message that contains personal data extracted from the medical image header is embedded into the LSBs of the MR image pixels by using six different methods. The support vector machine (SVM) is used as a classifier for steganalysis between cover and stego images. As a result of the analysis, the presence of hidden data in medical images is found with 99.28% accuracy and 0.9856 correlation coefficient value.