An improved dynamic discrete firefly algorithm for blind image steganalysis

Chhikara, Rita; Sharma, Prabha; Singh, Latika

doi:10.1007/s13042-016-0610-3

Cited by 27 publications

(22 citation statements)

References 28 publications

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“…The average results were used to prevent generation of a feature subset based on a single classifier. In [53], the C-SVM classifier using the Radial Basis Function kernel (RBF) was applied in the FA algorithm.…”

Section: Fitness Functionmentioning

confidence: 99%

“…The difference between Sigmoid and tanh functions is presented in Figure 6. The movement of fireflies in the FA algorithm [53] is done using Equation (11):…”

Section: Update and Move Agentsmentioning

confidence: 99%

“…For the same reason as in Table 2, we excluded the Group Hunting section. [49] FA [56] ABC [33,41] 13 GSO [50] FA [53] FA [58,81] ACO [82] ACO [83] Based on the survey, we can state that Bio-Medical Engineering is the most used field for algorithms' testing or applying. Researchers in [58] proposed the Chaos FA algorithm for heart disease prediction; the Bacterial Memetic Algorithm based feature selection for surface EMG-based hand motion recognition in long-term use was suggested by [85].…”

mentioning

confidence: 99%

“…The dynamic subfield is also image steganalysis. The researchers in [33] presented a novel method named IFAB based on an ABC algorithm for feature selection, and the authors in [53] used the FA algorithm for blind image steganalysis. Some other problems tackled in this field comprise fault diagnosis of complex structures with the BFO algorithm [64], skin detection based background removal for enhanced face recognition with adaptive BPSO [89], identifying malicious web domains with BPSO in [88], and web page classification with ACO [83].…”

mentioning

confidence: 99%

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Swarm Intelligence Algorithms for Feature Selection: A Review

2018

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The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem.

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Section: Fitness Functionmentioning

confidence: 99%

“…The difference between Sigmoid and tanh functions is presented in Figure 6. The movement of fireflies in the FA algorithm [53] is done using Equation (11):…”

Section: Update and Move Agentsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Swarm Intelligence Algorithms for Feature Selection: A Review

2018

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“…At present, there are mainly two types of steganographic security measure methods. The first type is from the attacker's perspective through blind steganalysis [2,3], which relies on a large number of training sets, many high-quality features, and good classifier. These security measure methods perform a certain degree of uncertainty and pool availability.…”

Section: Introductionmentioning

confidence: 99%

Security Measure for Image Steganography Based on High Dimensional KL Divergence

Song

Tang

Sun

et al. 2019

Security and Communication Networks

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Steganographic security is the research focus of steganography. Current steganography research emphasizes on the design of steganography algorithms, but the theoretical research about steganographic security measure is relatively lagging. This paper proposes a feasible image steganographic security measure based on high dimensional KL divergence. It is proved that steganographic security measure of higher dimensional KL divergence is more accurate. The correlation between neighborhood pixels is analyzed from the principle in imaging process and content characteristics, and it is concluded that 9-dimensional probability statistics are effective enough to be used as steganographic security measure. Then in order to reduce the computational complexity of high dimensional probability statistics and improve the feasibility of the security measure method, a security measure dimension reduction scheme is proposed by applying gradient to describe image textures. Experiments show that the proposed steganographic security measure method is feasible and effective and more accurate than measure method based on 4-dimensional probability statistics.

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Critical review of bio‐inspired data optimization techniques: An image steganalysis perspective

Johnvictor

Amalanathan

Venkata

et al. 2022

WIREs Data Min & Knowl

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Image steganalysis involves the discovery of secret information embedded in an image. The common method is blind image steganalysis, which is a twoclass classification problem. Blind steganalysis extracts all possible feature variations in an image due to embedding, select the most appropriate feature data, and then classifies the image. The dimensionality of the extracted image features are high and demand data reduction to identify the most relevant features and to aid accurate classification of an image. The classification is under two classes namely, clean (cover) image and stego (image with embedded

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An improved dynamic discrete firefly algorithm for blind image steganalysis

Cited by 27 publications

References 28 publications

Swarm Intelligence Algorithms for Feature Selection: A Review

Swarm Intelligence Algorithms for Feature Selection: A Review

Security Measure for Image Steganography Based on High Dimensional KL Divergence

Critical review of bio‐inspired data optimization techniques: An image steganalysis perspective

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