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 convolu...
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| Published in: | Security and communication networks Vol. 2021; pp. 1 - 9 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Hindawi
2021
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| 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. |
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| AbstractList | 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. 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. |
| Author | Mehedi, Ibrahim M. Shorfuzzaman, Mohammad Akther, Farzana Latif, Shaikh Abdul Aljohani, Abdulah Jeza Palaniswamy, Thangam Latif, Abdul Iskanderani, Ahmed I. |
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| Cites_doi | 10.1007/s12652-019-01493-x 10.1016/j.image.2020.116052 10.1016/j.neunet.2020.07.022 10.2352/issn.2470-1173.2017.7.mwsf-324 10.1109/tifs.2017.2710946 10.2352/issn.2470-1173.2018.07.mwsf-317 10.1016/j.ins.2020.02.048 10.1109/tifs.2020.3005304 10.1007/s00340-020-07480-x 10.1109/lsp.2018.2816569 10.1016/j.neucom.2020.02.105 10.1007/978-3-319-64185-0_20 |
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| Copyright | Copyright © 2021 Ahmed I. Iskanderani et al. Copyright © 2021 Ahmed I. Iskanderani et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| References | 22 23 24 26 S. Ozcan (2) M. Boroumand (6) 2018; 14 Y. Yuan (21) 2015; 20 A. Sharma (13) Z. Zhou (3) 2020; 14 R. Zhang (4) 2018 W. Ren (18) 2020; 401 11 15 16 K. Liu (14) 17 19 R. Zhang (12) 2019; 15 G. Huang (20) 1 J. Yang (10) 2017 M. Rezaei (25) 8 S. Wu (9) M. Yedroudj (7) S. N Gowda (5) 2020 |
| References_xml | – ident: 22 doi: 10.1007/s12652-019-01493-x – ident: 17 doi: 10.1016/j.image.2020.116052 – ident: 19 doi: 10.1016/j.neunet.2020.07.022 – ident: 15 doi: 10.2352/issn.2470-1173.2017.7.mwsf-324 – start-page: 1 ident: 14 article-title: Ensemble of cnn and rich model for steganalysis contributor: fullname: K. Liu – volume: 14 start-page: 4557 issue: 11 year: 2020 ident: 3 article-title: Ensemble deep learning features for real-world image steganalysis publication-title: KSII Transactions on Internet and Information Systems (TIIS) contributor: fullname: Z. Zhou – volume: 14 start-page: 1181 issue: 5 year: 2018 ident: 6 article-title: Deep residual network for steganalysis of digital images publication-title: Institute of Electrical and Electronics Engineers Transactions on Information Forensics and Security contributor: fullname: M. Boroumand – ident: 8 doi: 10.1109/tifs.2017.2710946 – ident: 26 doi: 10.2352/issn.2470-1173.2018.07.mwsf-317 – ident: 24 doi: 10.1016/j.ins.2020.02.048 – ident: 1 doi: 10.1109/tifs.2020.3005304 – year: 2017 ident: 10 article-title: Jpeg steganalysis based on densenet contributor: fullname: J. Yang – start-page: 1 ident: 25 article-title: Stegrt1: a dataset for evaluating steganalysis systems in real-world scenarios contributor: fullname: M. Rezaei – ident: 23 doi: 10.1007/s00340-020-07480-x – ident: 11 doi: 10.1109/lsp.2018.2816569 – start-page: 4700 ident: 20 article-title: Densely connected convolutional networks contributor: fullname: G. Huang – start-page: 1213 ident: 13 article-title: Spatial image steganalysis based on resnext contributor: fullname: A. Sharma – start-page: 1233 ident: 9 article-title: Steganalysis via deep residual network contributor: fullname: S. Wu – year: 2018 ident: 4 article-title: Efficient feature learning and multi-size image steganalysis based on cnn contributor: fullname: R. Zhang – volume: 401 start-page: 78 year: 2020 ident: 18 article-title: Learning selection channels for image steganalysis in spatial domain publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.02.105 contributor: fullname: W. Ren – volume-title: Stegcolnet: steganalysis based on an ensemble colorspace approach year: 2020 ident: 5 contributor: fullname: S. N Gowda – start-page: 2092 ident: 7 article-title: Yedroudj-net: an efficient cnn for spatial steganalysis contributor: fullname: M. Yedroudj – volume: 15 start-page: 1138 year: 2019 ident: 12 article-title: Depth-wise separable convolutions and multi-level pooling for an efficient spatial cnn-based steganalysis publication-title: Institute of Electrical and Electronics Engineers Transactions on Information Forensics and Security contributor: fullname: R. Zhang – start-page: 2280 ident: 2 article-title: Transfer learning effects on image steganalysis with pre-trained deep residual neural network model contributor: fullname: S. Ozcan – volume: 20 start-page: 16 issue: 1 year: 2015 ident: 21 article-title: A new dominance relation-based evolutionary algorithm for many-objective optimization publication-title: Institute of Electrical and Electronics Engineers Transactions on Evolutionary Computation contributor: fullname: Y. Yuan – ident: 16 doi: 10.1007/978-3-319-64185-0_20 |
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| SubjectTerms | Artificial intelligence Artificial neural networks Deep learning Digital imaging Genetic algorithms Machine learning Model accuracy Neural networks Performance measurement Sorting algorithms |
| Title | Artificial Intelligence-Based Digital Image Steganalysis |
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