A secure symmetric 3-D image encryption framework using quantum chaos and DNA-based encoding

The rise of 3-D data dependency across various fields, from medical imaging to virtual reality, requires robust security to prevent leakage or unauthorized access and to keep sensitive information secure from tampering. As 3-D models consist of floating-point numbers arranged in a specialized format, their protection necessitates a reliable encryption technique to safeguard sensitive data across various domains. In this paper, a symmetric encryption technique for 3-D models based on the quantum logistic map, Arnold cat map, improved logistic map, and DNA encoding is presented. Initially, the hash value derived from the SHA-256 function, combined with the plaintext data, is used as a symmetric key to establish the initial conditions for the chaotic system. Following this, the improved logistic map (ILM) is applied to introduce confusion among the coordinate values of plaintext data. Subsequently, the partially encrypted data is segmented into integer and fractional components. The integer matrix undergoes scrambling and diffusion through the complete framework of the quantum logistic map, Arnold cat map, DNA diffusion algorithm, and DNA complementary rule, enhancing the complexity of the encryption process. On the other hand, the fractional part is manipulated using the quantum logistic map. Finally, the transformed integer and fractional parts are systematically integrated to obtain the final encrypted data. The proposed algorithm undergoes rigorous testing on a variety of 3-D models, validating the effectiveness of the proposed encryption scheme by offering a high entropy value close to 8, 100% NPCR and 33.37% UACI values, near-zero correlation, strong resistance against differential attack, maintained computational efficiency, and high key sensitivity. Furthermore, encryption time varies linearly with the size of input 3-D model, demonstrating the applicability of the proposed algorithm for large-scale 3-D datasets.