Image encryption optimization algorithm based on GA-PSO and DNA convolutional code

Image encryption is essential for protecting sensitive data, yet achieving both high security and reliable recoverability under complex noisy channels remains challenging. To address this issue, we propose an image encryption optimization algorithm based on Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and DNA convolutional code. The enhanced GA-PSO combines the global search ability of GA with the fast convergence of PSO, enabling effective optimization of chaotic parameters and thereby improving diffusion quality and encryption performance. To improve robustness, DNA convolutional coding is introduced to utilize the redundancy of convolutional codes. During decoding, multiple candidate sequences are compared and the most probable original sequence is selected, enabling automatic error correction under complex channel conditions. Experimental results demonstrate that the proposed algorithm achieves a near-ideal entropy value of 7.9997 for the encrypted images, indicating high statistical randomness. Moreover, when the cropping ratio is below 19%, DNA convolutional coding reduces the error rate by up to 50% compared with encryption without error-correcting coding, demonstrating improved robustness. In summary, the proposed algorithm provides a robust and effective framework for reliable image encryption under noisy conditions.