Abstract
Image steganography involves the process of concealing sensitive information in the cover image to achieve secret communication. The counterpart of steganography is image steganalysis, which is used to detect any hidden information that is being communicated among different entities. The image steganalysis has gained much attention in the recent past from the information law enforcement as well as with the advancements in communication and information technology, the techniques available for steganography make it more challenging to detect steganographic content. Several steganalysis techniques are available as reported in the literature and each technique relies on the underlying steganographic method used. In this study, we expound an in-depth review of existing state-of-the-art steganalysis approaches and also discuss the evolving intelligent approaches i.e. deep learning (DL) that may be used for image steganalysis. We also present a comparative analysis of various benchmark datasets and evaluation metrics that are commonly used for image steganalysis algorithms. Besides, we also illustrate some openly available tools that may be used for the task of image steganalysis. Our analysis identifies several open research issues in this active field of DL-based image steganalysis and we also present different frameworks that exist in the literature. This study can serve as a reference document to the investigators for future directions in research on deep learning-based steganalysis.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Farooq, N., Selwal, A. Image steganalysis using deep learning: a systematic review and open research challenges. J Ambient Intell Human Comput 14, 7761–7793 (2023). https://doi.org/10.1007/s12652-023-04591-z
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DOI: https://doi.org/10.1007/s12652-023-04591-z