An efficient image steganography using spider monkey optimization

Image steganography, which encodes secret information into digital photos, is crucial for covert communication. It’s critical to strike a delicate balance between maintaining the altered image’s visual quality (imperceptibility) and concealing just the right amount of information (embedding rate). This paper proposes image steganography using Spider Monkey Optimization (SMO) in conjunction with Least Significant Bit (LSB) embedding, applied on 4-Cover Image Set of Lena-Pepper-Baboon-Jet of pixel size 256 x 256 to optimize pixel selection and reduce distortion to enhance embedding efficiency and security. It finds the optimal solution using SMO algorithm and inserts the message using least significant bit that exhibits the superior performance in order to imperceptibility and payload capacity, compared to Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) along with existing methods. Experimental results depict SMO score 4-6 percent and 10-16 percent in Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) over GA and PSO algorithms.