In recent years the popularity of smartphones has been on the rise, the importance of user experience is being discussed more frequently, and interface design is determined as a crucial technique in the interactive design of mobile devices. As both supply and demand for smartphones have grown, the interactive interface designs are obtained as significant, and attention is paid to their effects. The traditional method is only applicable to computers, has a limited user base, and has poor efficiency and effectiveness. However, the operation and functionality of mobile phones are diminished due to the installation of unwanted applications. Hence it is particularly evident in the interactive art approach in which there has been limited innovation in core capabilities and few distinguishing features among more established programmes. Inorder to perform speedy progress, the Convolutional Neural Network (CNN) and Deep Learning (DL) are widely determined. Furthermore, Style Transfer and Target Detection are emerging as popular because of their significance to the field of visual research. So this paper proposes the prevalent quasi- and flat-materialized style and analyzes the skills necessary for applications. The proposed method achieved superior performance when validated with MPI Sintel and Train2020 MSCOCO datasets.