Intelligent Carrier Allocation: A Cross-Modal Reasoning Framework for Adaptive Multimodal Steganography
Abhirup Das, Pranav Dudani, Shruti Sharma, Ravi Kumar C.V

TL;DR
This paper introduces an adaptive multimodal steganography framework using cross-modal reasoning to select optimal media carriers, enhancing security and data capacity over traditional fixed methods.
Contribution
It presents a novel Cross-Modal Reasoning engine that evaluates multiple media types for steganography and adaptively allocates data based on their reliability scores.
Findings
The framework improves security against steganalysis.
It achieves higher data embedding capacity.
It outperforms static multimodal steganography methods.
Abstract
In today's digital world, which has many different types of media, steganography, the art of secret communication, has a lot of problems to deal with. Traditional methods are often fixed and only work with one type of carrier media. This means they don't work well with all the different types of media that are out there. This system doesn't send data to "weak" or easily detectable carriers because it can't adapt. This makes the system less safe and less secret in general. This paper proposes a novel Intelligent Carrier Allocation framework founded on a Cross-Modal Reasoning (CMR) Engine. This engine looks at a wide range of carriers, such as images, audio, and text, to see if they are good for steganography. It uses important measurements like entropy, signal complexity, and vocabulary richness to come up with a single reliability score for each modality. The framework uses these scores…
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Taxonomy
TopicsAdvanced Steganography and Watermarking Techniques · Chaos-based Image/Signal Encryption · Computer Science and Engineering
