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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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A deep learning-driven multi-layered steganographic approach for enhanced data security.

Yousef Sanjalawe¹, Salam Al-E'mari², Salam Fraihat³

¹Department of Information Technology, King Abdullah II School for Information Technology, University of Jordan (JU), Amman, 11942, Jordan.

Scientific Reports

|February 8, 2025

Summary

This summary is machine-generated.

This study introduces a novel steganographic framework combining Huffman coding, LSB embedding, and deep learning for secure data hiding. The method enhances imperceptibility, robustness, and security in digital communications.

Keywords:

Data security Huffman encoding Image embedding LSB embedding Steganography

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Area of Science:

Computer Science
Information Security
Digital Forensics

Background:

Ensuring data integrity, authenticity, and confidentiality is crucial in the digital age due to increasing connectivity and security threats.
Traditional steganographic methods face limitations including low payload capacity, detectability, and vulnerability to attacks.

Purpose of the Study:

To address the limitations of traditional steganography by proposing a novel multi-layered framework.
To enhance imperceptibility, robustness, and security in data hiding techniques.

Main Methods:

Integration of Huffman coding for data compression and statistical obfuscation.
Least Significant Bit (LSB) embedding for efficient data insertion into cover images.
A deep learning-based encoder-decoder model for enhanced security and imperceptibility.

Main Results:

High visual fidelity demonstrated by Structural Similarity Index Metrics (SSIM) above 99%.
Achieved 100% text recovery accuracy under standard conditions, indicating robust data retrieval.
Significantly improved resistance to common attacks like noise and compression compared to traditional methods.

Conclusions:

The proposed multi-layered framework offers superior robustness, security, and computational efficiency for data hiding.
This innovative approach advances secure communication and digital rights management by effectively addressing modern data hiding challenges.
The combination of compression, adaptive embedding, and deep learning provides a balanced solution for imperceptibility and resilience in steganography.