An enhanced Huffman-PSO based image optimization algorithm for image steganography

It is crucial in the field of image steganography to find an algorithm for hiding information by using various combinations of compression techniques. The primary factors in this research are maximizing the capacity and improving the quality of the image. The image quality cannot be compromised up to a certain level as it breaks the concept of steganography by getting distorted visibly. The second primary factor is maximizing the data-carrying/embedding capacity, which makes the use of this technique more efficient. In this paper, we are proposing an image steganography tool by using Huffman Encoding and Particle Swarm Optimization, which will improve the performance of the information hiding scheme and improve overall efficiency. The combinational technique of Huffman PSO not only offers higher information embedment capabilities but also maintains the image quality. The experimental analysis and results on cover images along with different sizes of secret messages validate that the proposed HPSO scheme has superior results using parameters Peak-Signal-to-Noise-Ratio, Mean Square Error, Bit Error Rate, and Structural Similarity Index. It is also robust against statistical attacks.

Automated summary

Finding an efficient algorithm for image steganography that maximizes information capacity while maintaining image quality is crucial. The proposed HPSO scheme, utilizing a combination of Huffman Encoding and Particle Swarm Optimization, shows superior results in experimental analysis and robustness against statistical attacks.