New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and...

Area of Science:

• Computer Science
• Information Security
• Digital Image Processing

Background:

• Steganography algorithms face challenges in balancing secret message size and stego-image quality.
• Existing methods often struggle to embed large amounts of data without compromising image imperceptibility.

Purpose of the Study:

• To propose a new steganography algorithm, the Multi-Channel Deep Data Hiding and Extraction Algorithm (MCDHEA).
• To enhance the capacity for hiding large secret messages in cover images with high imperceptibility.
• To improve the robustness and security of hidden data against extraction attempts.

Main Methods:

• The algorithm employs a modified multi-level steganography (MLS) approach with a new modification of the least significant bits (NMLSB).
• Secret messages are distributed across n-blocks, hidden in sub-channels, and then integrated into a main channel.
• Lossless image compression (Huffman coding) and XOR encryption are applied at various stages.
• A non-uniform bit replacement strategy is used in the final level of the main channel.

Main Results:

• The proposed MCDHEA algorithm demonstrates superior performance compared to previous works in terms of imperceptibility and robustness.
• Achieved a maximum Peak Signal-to-Noise Ratio (PSNR) of 61.2 dB for a payload of 18,750 bytes.
• Attained a maximum Video Information Fidelity (VIF) of 0.95 for a payload of 19,660 bytes and a maximum Structural Similarity Index Measure (SSIM) of 0.999 for a payload of 294,912 bytes.

Conclusions:

• The MCDHEA algorithm offers an effective solution for high-capacity, high-quality steganography.
• The novel approach provides enhanced security and robustness for hidden data.
• The results validate the algorithm's effectiveness in practical steganographic applications.