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Document Type

Article

Keywords

Classification, Feature Extraction, Feature Selection, Frequency Domain, Metaheuristic Algorithms, Spatial Domain, Steganalysis, Steganography

Abstract

Due to the widespread popularity of digital images on the Internet, image-based steganography has become a widely adopted technique for embedding secret information into everyday visual content. In parallel, steganalysis plays a vital role in digital forensics and information security by seeking to uncover hidden content within these images. Although steganographic techniques—particularly those employing adaptive embedding strategies—have made significant progress, many steganalysis approaches still struggle to generalize effectively across different image types and embedding methods. This contrast highlights the need for more intelligent, flexible, and robust analysis frameworks. This review examines steganographic techniques for digital images and the application of metaheuristic algorithms in steganalysis. These algorithms are employed in tasks such as feature selection and parameter optimization. They can also function as classifiers themselves, thereby enhancing the detection of hidden information. We conducted a structured review of over 100 research articles, categorizing steganographic approaches based on their embedding domain (spatial and frequency) and steganalysis techniques according to the metaheuristic algorithms they utilize. Metaheuristic algorithms have demonstrated significant promise in improving the effectiveness of steganalysis by optimizing both feature selection and classification processes. However, their performance is often influenced by factors such as parameter tuning, initialization strategies, and the quality of extracted features. Recent studies also show a growing trend toward hybrid and ensemble-based techniques, which further enhance detection accuracy and reliability.

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