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

Abhirup Das, Pranav Dudani, Shruti Sharma, Ravi Kumar C.

TL;DR

The paper tackles the instability of fixed, single-modal steganography by proposing Intelligent Carrier Allocation (ICA) driven by a Cross-Modal Reasoning (CMR) Engine. A unified reliability score, computed as $Reliability = 0.4 \times Robustness + 0.3 \times Imperceptibility + 0.2 \times Entropy + 0.1 \times Complexity$, guides adaptive payload distribution across image, audio, and text carriers, enabling smarter, more secure concealment. Through a two-phase process—Intelligent Encoding and Validated Extraction—the framework achieves higher imperceptibility and robustness compared to static baselines, with empirical results showing reduced BER under attack and more efficient use of high-quality carriers. This cross-modal, reasoning-based approach offers a scalable path toward resilient, covert communication across diverse multimedia environments.

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.

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

TL;DR

The paper tackles the instability of fixed, single-modal steganography by proposing Intelligent Carrier Allocation (ICA) driven by a Cross-Modal Reasoning (CMR) Engine. A unified reliability score, computed as , guides adaptive payload distribution across image, audio, and text carriers, enabling smarter, more secure concealment. Through a two-phase process—Intelligent Encoding and Validated Extraction—the framework achieves higher imperceptibility and robustness compared to static baselines, with empirical results showing reduced BER under attack and more efficient use of high-quality carriers. This cross-modal, reasoning-based approach offers a scalable path toward resilient, covert communication across diverse multimedia environments.

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.

Paper Structure

This paper contains 16 sections, 1 equation, 10 figures.

Table of Contents

  1. Introduction
  2. Literature Review
  3. The growth of steganography and adaptive carrier selection
  4. Progress in Cross-Modal Integration and Multimodal Steganography
  5. System-Level Hybridization and Research Hurdles
  6. Research Gap and Motivation
  7. METHODOLOGY: THE CROSS-MODAL REASONING FRAMEWORK
  8. Phase 1: Intelligent Encoding Process
  9. Phase 2: Validated Extraction Process
  10. Results and Discussion
  11. Setting up the experiment
  12. Core Finding 1: Intelligent Bit Distribution
  13. Core Finding 2: Adaptive Imperceptibility
  14. Core Finding 3: Superior System Robustness
  15. Results and Discussion
  16. ...and 1 more sections

Figures (10)

  • Figure 1: Proposed system architecture, showing the Cross-Modal Reasoning (CMR) Engine analyzing Image, Audio, and Text carriers to create an optimized, distributed payload.
  • Figure 2: Depiction of the Intelligent Coding Process with Carrier Ingestion and Analysis
  • Figure 3: Calculation of Reliability score
  • Figure 4: Visualization of the Adaptive Error Correction
  • Figure 5: Efficiency of ICA as compared to Static Allocation
  • ...and 5 more figures