Deep Learning-Driven DNA Image Encryption with Optimal Chaotic Map Selection

This research introduces an advanced image encryption framework addressing critical security limitations in existing approaches. The study focuses on developing a robust encryption methodology that overcomes arbitrary chaotic map selection and static key generation vulnerabilities. Our approach integrates three synergistic components: a systematic chaotic map evaluation protocol identifying optimal dynamic systems, a deep learning-based key generation mechanism employing fine-tuned convolutional neural networks for image-sensitive cryptographic keys, and a hybrid encryption pipeline combining DNA encoding with chaotic diffusion. Experimental validation demonstrates that the proposed scheme achieves near-ideal entropy values (cipher images with an average entropy of 7.90 and above), and ensures extremely low correlation coefficients between adjacent pixels (close to zero in horizontal, vertical, and diagonal directions). Differential analysis confirms strong robustness, with NPCR values exceeding 99.6% and UACI about 33.5% across multiple color images. Visual results show that encrypted images display no perceivable patterns or similarities with the original images. Comparative performance assessment also highlights the method’s efficiency, with encryption execution times competitive with or better than recent state-of-the-art methods. Brute-force resistance is guaranteed by an extensive key space determined by the combination of deep learning-generated keys, Lorenz chaotic parameters, and DNA encoding rule permutations. The comprehensive multi-layered security strategy further ensures resilience against brute-force, statistical, differential, and chosen-plaintext attacks, as well as against modern deep learning-based cryptanalysis.