Diversity Matters: Dataset Diversification and Dual-Branch Network for Generalized AI-Generated Image Detection

TL;DR

This paper introduces a novel framework emphasizing data diversity and dual-branch feature extraction to improve the robustness of AI-generated image detection across various models and datasets.

Contribution

The work proposes a feature-domain similarity filtering mechanism and a dual-branch network combining CLIP features from pixel and frequency domains for enhanced detection.

Findings

Significantly improves cross-model detection performance.

Enhances generalization to unseen generative models.

Demonstrates robustness across diverse datasets.

Abstract

The rapid proliferation of AI-generated images, powered by generative adversarial networks (GANs), diffusion models, and other synthesis techniques, has raised serious concerns about misinformation, copyright violations, and digital security. However, detecting such images in a generalized and robust manner remains a major challenge due to the vast diversity of generative models and data distributions. In this work, we present \textbf{Diversity Matters}, a novel framework that emphasizes data diversity and feature domain complementarity for AI-generated image detection. The proposed method introduces a feature-domain similarity filtering mechanism that discards redundant or highly similar samples across both inter-class and intra-class distributions, ensuring a more diverse and representative training set. Furthermore, we propose a dual-branch network that combines CLIP features from the pixel domain and the frequency domain to jointly capture semantic and structural cues, leading to improved generalization against unseen generative models and adversarial conditions. Extensive experiments on benchmark datasets demonstrate that the proposed approach significantly improves cross-model and cross-dataset performance compared to existing methods. \textbf{Diversity Matters} highlights the critical role of data and feature diversity in building reliable and robust detectors against the rapidly evolving landscape of synthetic content.