AI-Generated Image Detection With Wasserstein Distance Compression and Dynamic Aggregation

With the rapid advancement of generative models, image detectors for AI-generated content have become an increasingly necessary technology in computer vision, attracting significant attention from researchers. This technology aims to detect whether an image is naturally generated by imaging systems (e.g., digital cameras) or generated by advanced AI techniques. Despite the promising performance achieved by recent fake detection methods, they are typically trained on millions of redundant images with similar characteristics, leading to inefficient training. Furthermore, the performances of existing detectors often deteriorate when the training datasets are imbalanced. To address these challenges, we propose a novel AI-generated image detector based on dynamic aggregation and information compression with the Wasserstein distance. Experimental results show that our proposed method significantly outperforms state-of-the-art models that generalize across different generative models, with an increase of +1.86% average accuracy and +0.14% average precision, while substantially reducing the training time. On imbalanced datasets, our proposed method leads to a +14.46% accuracy improvement, clearly demonstrating its robustness on imbalanced datasets.