Intelligent Carrier Allocation: A Cross-Modal Reasoning Framework for Adaptive Multimodal Steganography

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 to fairly and intelligently share the secret bitstream, giving more data to carriers that are thought to be stronger and more complex. This adaptive allocation strategy makes the system as hard to find as possible and as strong as possible against steganalysis. We demonstrate that this reasoning-based approach is more secure and superior in data protection compared to static, non-adaptive multimodal techniques. This makes it possible to build stronger and smarter secret communication systems.