Recent advances in mathematical cryptography and bio-inspired computing have motivated the development of RNA-based encryption frameworks for secure image protection. In this work, we propose a novel image encryption scheme that combines an enhanced Feistel network with a refined Vigenère cipher, while preserving their fundamental principles. The system is strengthened through two newly designed substitution tables and advanced confusion-diffusion mechanisms, achieving a balanced trade-off between randomness, nonlinearity, and reversibility. The proposed approach is built on the finite field \(F_{64}\) , constructed using an irreducible polynomial of degree six, and extended to a two-dimensional algebraic structure that enables efficient codon-level transformations. In contrast to conventional algebraic settings, this structure provides a richer operational framework for secure encoding. Furthermore, the integration of chaotic maps, namely the 2D Logistic and Skew tent maps, enhances the generation of pseudorandom sequences, thereby improving key sensitivity and system unpredictability. Extensive experiments conducted on standard benchmark images (e.g., Lena, Baboon, and Brain) demonstrate the effectiveness of the proposed scheme. The method achieves an average information entropy of 7.9997, an NPCR of 99.60%, and a UACI of 33.49%, indicating strong diffusion and sensitivity properties. In addition, the correlation coefficients between adjacent encrypted pixels are close to zero, confirming effective decorrelation. The computational cost remains competitive for 256 \(\times\) 256 \(\times\) 3 color images. Comparative analysis with recent chaotic and hybrid encryption methods shows that the proposed RNA-Feistel-Vigenère framework provides improved security performance and robustness against statistical and differential attack, making it a suitable candidate for secure image transmission and storage.
