Generated Image Classification Model for Deep Learning-based Inpainting Model

Thanks to image generation models (e.g., DALL-E 2) that have shown high generation performance, the generated image data has been widely used in computer vision research, which looks natural from the human perspective. In this paper, we start from the assumption that the generated images may be unstable from the perspective of deep learning models. In particular, for the inpainting task of seamlessly restoring objects or areas in an image the inpainting model may not show the excellence on generated images. Through the experiments, we demonstrate the vulnerability of the inpainting model to the generated images, and present real and generated image classification framework for future seamless inpainting research.