Detection of Fake Images Focused on Few Local Blocks

In this article, we propose the construction of a Convolutional Neural Network (CNN) model as a novel approach to detect fake images generated by diffusion models. However, constructing a CNN model to efficiently detect high-quality fake images may consume substantial computational resources, emphasizing the need for the development of a computationally inexpensive, lightweight model.At the crux of our research is the inquiry into whether a CNN can accurately detect counterfeit images produced by diffusion models using only partial image information. Further, to elucidate how this detection mechanism specifically functions, we delve deeply into the attention mechanism of the CNN.Experimental results indicate that the CNN places particular emphasis on edge information in its image analysis. This could potentially be key to effectively pinpointing specific features of counterfeit images generated by diffusion models.