The paper proposes a method of extracting the feature vector of images, which makes it possible to effectively detect the presence of hidden information in JPEG images embedded by various popular steganography tools. This method is based on the usage of the transition probability matrix. The essence of the method for extracting the feature vector of the image is to use the transition probability matrix and apply the image calibration method to improve the accuracy of steganalysis and reduce the number of false positives. For each image from the training and test sets a feature vector is found in this way, the number of elements is 324. Further, the models were trained on the training dataset by each of machine learning methods separately: decision trees with gradient boosting, linear models, k-nearest neighbors, support vector machines, neural networks, and artificial immune systems. To assess the capacity of the models the following metrics were used: accuracy, the rate of the false positive and false negative errors, and the confusion matrix. The results of classification by each of the above methods are given. For training and testing a dataset IStego100K was used, which consists of 208 thousand images of the same size 1024 x 1024 with different quality values in the range from 75 to 95. One of the J-UNIWARD, nsF5, and UERD steganography algorithms was used to embed a hidden message. As a result, we can observe that the proposed approach to extracting the feature vector makes it possible to detect the presence of hidden information embedded by non-adaptive steganography (Steghide, OutGuess and nsF5) in static JPEG images with high accuracy (more than 95%). However, for adaptive steganography methods (J-UNIWARD, UERD) the accuracy is less (about 50-60%).

The purpose of this work is to develop the steganalysis method of static JPEG images, based on the usage of artificial immune systems.A model of an artificial immune system was developed for the problem of detecting hidden information in JPEG images. Basic requirements were determined, and basic elements of an artificial immune system were considered, mutation and antibody cloning operations were introduced. Also, formal description of main nodes of the artificial immune system is presented. In addition, a brief overview and analysis of the state of the steganalysis problem is provided in the paper. Furthermore, analysis of the obtained experimental results and an assessment of the effectiveness are performed for the developed method.The proposed method allows to detect the presence of hidden information, embedded by various popular steganography tools (like OutGuess, Steghide and F5) in static JPEG images with a sufficiently high accuracy. The theoretical significance of this work consists in the development of a fairly promising approach of heuristic steganalysis using artificial immune systems. The practical significance lies in the developed software product, as well as in experimental data, that confirms the effectiveness of the steganalysis method towards the detection of hidden information in JPEG images.

The purpose of this work is to develop the steganalysis method of static JPEG images, based on the usage of artificial immune systems.A model of an artificial immune system was developed for the problem of detecting hidden information in JPEG images. Basic requirements were determined, and basic elements of an artificial immune system were considered, mutation and antibody cloning operations were introduced. Also, formal description of main nodes of the artificial immune system is presented. In addition, a brief overview and analysis of the state of the steganalysis problem is provided in the paper. Furthermore, analysis of the obtained experimental results and an assessment of the effectiveness are performed for the developed method.The proposed method allows to detect the presence of hidden information, embedded by various popular steganography tools (like OutGuess, Steghide and F5) in static JPEG images with a sufficiently high accuracy. The theoretical significance of this work consists in the development of a fairly promising approach of heuristic steganalysis using artificial immune systems. The practical significance lies in the developed software product, as well as in experimental data, that confirms the effectiveness of the steganalysis method towards the detection of hidden information in JPEG images.