Research of main components of machine learning based JPEG-stegananalysis systems - Кошкіна, Наталія Василівна (Ukrainian Information Security Research Journal, 2020)
To create effective steganalysis systems in the given practical conditions it is necessary to perform analysis and quality estimation of existing methods and components. To select optimal system components it is required to compare the estimates of basic characteristics of the candidates available. However, making such comparison based on a data from scientific publications is quite difficult due to differences of the conditions of numerical experiments. The basis of this study is the principle of creating equal conditions for all the investigated statistical features based models for JPEG steganalysis using machine-learning methods. We analyzed the detection performance and accuracy for four different variants of data hiding in the frequency domain that were obtained using statistical models such as CHEN, CC-CHEN, LIU, CC-PEV, CC-C300, GFR and DCTR, as well as SVM with linear or Gaussian kernel functions or ensemble classifier. The main results of the study are tables containing numerical estimates of performance of the main stages of steganoanalysis and the classification accuracy of empty and stego images. Model LIU was the slowest for extraction of features vector, but it is the fastest in the classifier training process. Models CC-PEV, LIU and DCTR combined with the ensemble classifier provided the best detection accuracy for stego images created by programs like Jsteg, Jphide and Steganos Privacy Suite. The detection accuracy of stego images by Jsteg, Jphide and Steganos in our experiments reached 100, 95.3 and 99.8% accordingly. J-UNIWARD method has proved itself to be more secure than LSB-steganography. The GFR and DCTR models are proved to be the most sensitive to J-UNIWARD transformation. In distinction to other investigated models they analyze statistics in the spatial domain of the image but not in the frequency domain - where the message was embedded.