LDR-Net: A Novel Framework for AI-generated Image Detection via Localized Discrepancy Representation

TL;DR

LDR-Net is a new framework that detects AI-generated images by analyzing local anomalies like unnatural smoothness and textures, achieving high accuracy and generalization across different generative models.

Contribution

It introduces LDR-Net, combining local gradient autocorrelation and local variation pattern modules to effectively identify local discrepancies in generated images.

Findings

Achieves state-of-the-art detection accuracy.

Generalizes well to unseen generative models.

Effectively captures local anomalies like smoothing and irregular textures.

Abstract

With the rapid advancement of generative models, the visual quality of generated images has become nearly indistinguishable from the real ones, posing challenges to content authenticity verification. Existing methods for detecting AI-generated images primarily focus on specific forgery clues, which are often tailored to particular generative models like GANs or diffusion models. These approaches struggle to generalize across architectures. Building on the observation that generative images often exhibit local anomalies, such as excessive smoothness, blurred textures, and unnatural pixel variations in small regions, we propose the localized discrepancy representation network (LDR-Net), a novel approach for detecting AI-generated images. LDR-Net captures smoothing artifacts and texture irregularities, which are common but often overlooked. It integrates two complementary modules: local gradient autocorrelation (LGA) which models local smoothing anomalies to detect smoothing anomalies, and local variation pattern (LVP) which captures unnatural regularities by modeling the complexity of image patterns. By merging LGA and LVP features, a comprehensive representation of localized discrepancies can be provided. Extensive experiments demonstrate that our LDR-Net achieves state-of-the-art performance in detecting generated images and exhibits satisfactory generalization across unseen generative models. The code will be released upon acceptance of this paper.