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

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.