The neural distinguisher (ND) is the combined product of differential cryptanalysis and deep learning. Its emergence has greatly promoted the development of differential cryptanalysis. Current approaches to improving the performance of NDs focus on data input formats and training frameworks. However, many researchers independently focused on enhancing the data input format or training framework, neglecting their adaptability to each other. Additionally, little research has focused on improving the data input format based on its correlation with the components of the cipher. This paper proposes...
