Feature selection for image steganalysis using levy flight-based grey wolf optimization

Pathak, Yadunath; Arya, K. V.; Tiwari, Shailendra

doi:10.1007/s11042-018-6155-6

Cited by 66 publications

(30 citation statements)

References 37 publications

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“…Sharma et al [171] proposed a modified version of GWO for identifying the symptoms of Parkinson's disease with random forest, KNN and decision tree classifier. Pathak et al [172] proposed levy flight GWO which was used to select the relevant features from the original datasets. It used random forest classifier for image steg analysis and applied to Bossbase ver 1.01 datasets.…”

Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

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Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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“…1. The proposed model follows the training sequence of HLSTM [39] at word level. The applied embedding technique helps in retrieving the hatred words.…”

Section: Design Principlesmentioning

confidence: 99%

Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text

Shekhar

Garg

Agrawal

et al. 2021

Complex Intell. Syst.

109

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The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model is trained to identify the hatred and trolling words available in social media contents. The proposed HLSTM systems model is equipped with self-learning and predicting mechanism for annotating hatred words in transliteration domain. The Hindi–English data are ordered into Hindi, English, and hatred labels for classification. The mechanism of word embedding and character-embedding features are used here for word representation in the sentence to detect hatred words. The method developed based on HLSTM model helps in recognizing the hatred word context by mining the intention of the user for using that word in the sentence. Wide experiments suggests that the HLSTM-based classification model gives the accuracy of 97.49% when evaluated against the standard parameters like BLSTM, CRF, LR, SVM, Random Forest and Decision Tree models especially when there are some hatred and trolling words in the social media data.

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“…The classifier then determines whether an image contains an attachment or not. Examples of such steganalysis technique are described in [11][12][13].…”

Section: B Key Embedding Performance Indicatorsmentioning

confidence: 99%

Digital Steganography and Watermarking for Digital Images: A Review of Current Research Directions

2020

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The development of information technology has led to a significant increase in the share of multimedia traffic in data networks. This has necessitated to solve the following information security tasks in relation to multimedia data: protection against leakage of confidential information, as well as identifying the source of the leak; ensuring the impossibility of unauthorized changes; copyright protection for digital objects. To solve such kind of problems, methods of steganography and watermarking are designed that implement embedding in digital objects hidden information sequences for various purposes. In this paper, an overview of promising research in the specified area is provided. First of all, we provide basic information about this field of research and consider the main applications of its methods. Next, we review works demonstrating current trends in the development of methods and algorithms for data hiding in digital images. This review is not exhaustive; it focuses on contemporary works illustrating current research directions in the field of information embedding in digital images. This is the main feature of review, which distinguishes it from previously published reviews. The paper concludes with an analysis of identified problems in the field of digital steganography and digital watermarking.

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Feature selection for image steganalysis using levy flight-based grey wolf optimization

Cited by 66 publications

References 37 publications

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text

Digital Steganography and Watermarking for Digital Images: A Review of Current Research Directions

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