Chaotic cosine and logistic map based robust image encryption with dual-stage confusion-diffusion architecture

This study presents an efficient image encryption algorithm designed for secure data transmission in big data environments. The proposed method employs a cosine extended logistic chaotic map with a wider chaotic range and enhanced randomness. The map's dynamics are verified through bifurcation diagrams, Lyapunov exponents, Shannon entropy and Kolmogorov entropy. The encryption scheme incorporates two confusion and two diffusion phases using chaotic pixel permutation, controlled flipping, modulo arithmetic, MSB/LSB separation, and cross quadrant bitwise operations to achieve lightweight yet robust protection suitable for resource constrained systems. Experimental results on standard USC-SIPI and medical image datasets show near ideal entropy (~ 7.997), NPCR and UACI values close to 99.6094% and 33.4635%, and chi square and correlation values within secure limits, indicating strong resistance against statistical and differential attacks. With an estimated key space of 2³¹⁸, the scheme surpasses brute force resilience standards and demonstrates an effective balance between security and computational efficiency for real time image transmission.