A DNA mutation image encryption scheme based on neural networks and improving fractional chaotic systems

This study presents an encryption algorithm with strong anti-interference capabilities, which is implemented within neural networks. This algorithm integrates the dynamic random length confusion of DNA strands, diffusion based on DNA mutations, and a custom-constructed neural network architecture. Initially, pixel confusion and diffusion operations on the original image are implemented by chaotic sequences produced by the G4CCS system. Subsequently, dynamically encoded DNA sequences are created from the processed data, followed by random-length DNA chain confusion guided by chaotic sequences. The encoded data are diffused through the established DNA mutation rules. DNA decoding yields the encrypted image. Finally, the encryption scheme is implemented through a specialized neural network layer, while the decryption network adopts an inverse process enhanced with an additional anti-interference layer, significantly improving attack resistance. Rigorous security analysis and statistical evaluations demonstrate that our encryption scheme effectively withstands various cryptographic attacks, showing substantial improvements in both security performance and implementation efficiency.