Artificial Intelligence-Based Digital Image Steganalysis. (21st April 2021)

• Record Type: Journal Article
• Authors: Iskanderani, Ahmed I.; Mehedi, Ibrahim M.; Aljohani, Abdulah Jeza; Shorfuzzaman, Mohammad; Akther, Farzana; Palaniswamy, Thangam; Latif, Shaikh Abdul; Latif, Abdul
• Other Names: Kaur Manjit Academic Editor.
• Is Part Of: Security and communication networks. Volume 2021(2021)
• Publication Date: 2021-04-21
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2021/9923389
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : 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.