Artificial Intelligence-Based Digital Image Steganalysis

Recently, deep learning-based models are being extensively utilized for steganalysis. However, deep learning models suffer from overfitting and hyperparameter tuning issues. Therefore, in this paper, an efficient θ-nondominated sorting genetic algorithm-(θ NSGA-) III based densely connected convolutional neural network (DCNN) model is proposed for image steganalysis. θ NSGA-III is utilized to tune the initial parameters of DCNN model. It can control the accuracy and f-measure of the DCNN model by utilizing them as the multiobjective fitness function. Extensive experiments are drawn on STEGRT1 dataset. Comparison of the proposed model is also drawn with the competitive steganalysis model. Performance analyses reveal that the proposed model outperforms the existing steganalysis models in terms of various performance metrics.