Machine Vision, Prompts and Neural Network Structure in Art: Reverse Engineering in Image Generation

The paper proposes the use of reverse engineering and artistic creation perspective to explore the convergence of machine vision and neural networks for generating images that closely resemble the original objects. With the rapid and significant development of machine learning and neural networks, these technologies have garnered significant attention in recent years due to their image generation and prompt text functions, which offer new possibilities. Rather than utilizing these technologies solely for creating new works, it is important to investigate their potential for generating images that resemble the original objects. By converging different technologies in the era of machine intelligence, it is possible to achieve greater flexibility and adaptability in image generation processes, leading to a wider range of potential outcomes. It is hoped that it can shed light on the possibility of generating images in particular using artistic approaches.