A Competent Medical Image Steganography using Improved Optimization Algorithm with Huffman Encoding Techniques

Steganography is considered a reliable solution for avoiding third-party attacks. It is the process of embedding confidential information in another image. So that, they can be sent to the user's end with full security. When it comes to data privacy and authentication, steganography techniques...

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Bibliographic Details
Published in:2023 7th International Conference on Computing Methodologies and Communication (ICCMC) pp. 1065 - 1073
Main Authors: Ramapriya, B., Kalpana, Y.
Format: Conference Proceeding
Language:English
Published: IEEE 23.02.2023
Subjects:
Compression
Double Matrix XOR encoding
DT-CWT (Dual Tree Complex Wavelet Transform)
Huffman Coding
Image coding
Medical Image Steganography
Particle swarm optimization
Reliability
Security
SSOA (Salp Swarm Optimization Algorithm)
Steganography
Wavelet transforms
Wireless networks
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Summary:Steganography is considered a reliable solution for avoiding third-party attacks. It is the process of embedding confidential information in another image. So that, they can be sent to the user's end with full security. When it comes to data privacy and authentication, steganography techniques are essential. This proposed medical image steganography procedure by exploiting Dual Tree-Complex Wavelet Transform based transform and image encryption procedure were proposed in this proposed work. Then an improved SSOA optimization algorithm is engaged to detect smooth edge blocks. As a result, the selection of pixels for embedding is facilitated. Embedding the Secret data into the cover image is then done using a double matrix XOR encoding. After the embedding process, the stego image is produced. The Stego output image is further compressed with Huffman Coding which produces the stego Compressed image. The stego image is compressed for fast transmission of secret data via the wireless network. Thus, the proposed method shows the best results with high payz load capacity, security, and image quality than the existing methods. Testing was performed on PSNR, MSE, IF, and SSIM metrics to verify the performance of proposed methodologies.
DOI:10.1109/ICCMC56507.2023.10083698