AI-based Optimization for Automatic Image Steganalysis

MAster assignment

Type: Master CS

Period: Start date: as soon as possible

Student: Unassigned

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Abstract:

The rise of image steganography for covert communication has increased the need for more eﬀective steganalysis methods to detect hidden messages in images. However, existing steganalysis techniques may struggle to accurately identify hidden data in images that have been encoded using advanced steganography methods. This research proposes an AI-based enhanced optimization framework for automatic image steganalysis, integrating socio-inspired/nature-based/Meta-heuristic optimization algorithm(s) with machine learning and deep learning techniques. The goal is to automatically optimize the feature selection process and improve the accuracy of steganalysis in detecting hidden bits in images. The outcome provides a more scalable and eﬃcient solution for digital forensics and cybersecurity. It will be a system that can automatically detect stego-images, enhancing its ability to ﬁnd hidden information in various image types and steganography methods, including LSB and DCT.

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