Performance Comparison and Visualization of AI-Generated-Image Detection Methods

Abstract

Recent advancements in artificial intelligence (AI) have revolutionized the field of image generation. This has concurrently escalated social problems and concerns related to AI image generation, underscoring the necessity for an effective AI-generated-image detection method. Therefore, numerous methods for detecting AI-generated images have been developed, but there remains a need for research comparing the effectiveness of and visualizing these detection methods. In this study, we classify AI-generated-image detection methods by the image features they use and compare their generalization performance in detecting AI-generated images of different types. We selected five AI-generated-image detection methods for performance evaluation and selected vision transformer as an additional method for comparison. We use two types of training datasets, i.e., ProGAN and latent diffusion; combine existing AI-generated-image test datasets into a diverse test dataset; and divide them into three types of generative models, i.e., generative adversarial network (GAN), diffusion, and transformer, to evaluate the comprehensive performance of the detection methods. We also analyze their detection performance on images with data augmentation, considering scenarios that make it difficult to detect AI-generated images. Grad-CAM and t-SNE are used to visualize the detection area and data distribution of each detection method. As a result, we determine that artifact-feature-based detection performs well on GAN and real images, whereas image-encoder-feature-based detection performs well on diffusion and transformer images. In summary, our research analyzes the comparative detection performance of various AI-generated-image detection methods, identifies their limitations, and suggests directions for further research.