GenShield: Unified Detection and Artifact Correction for AI-Generated Images

2026-05-15

AI Summary

This work addresses the growing challenge of verifying the authenticity of increasingly realistic AI-generated images, a task for which existing methods struggle to jointly perform detection and artifact correction. The paper proposes GenShield, the first unified autoregressive framework that integrates detection and controllable artifact repair through a synergistic “diagnose-and-repair” mechanism operating in a closed loop, enabling interpretable detection and targeted restoration. GenShield innovatively establishes a mutually reinforcing relationship between detection and repair, incorporating a Visual Chain-of-Thought curriculum learning strategy that supports multi-step self-explanatory refinement with an explicit stopping criterion. Evaluated on both established detection benchmarks and a newly curated large-scale dataset of artifact–repair image pairs using a comprehensive protocol, GenShield achieves state-of-the-art performance and demonstrates exceptional generalization across diverse generation models.

Abstract

Diffusion-based image synthesis has made AI-generated images (AIGI) increasingly photorealistic, raising urgent concerns about authenticity in applications such as misinformation detection, digital forensics, and content moderation. Despite the substantial advances in AIGI detection, how to correct detected AI-generated images with visible artifacts and restore realistic appearance remains largely underexplored. Moreover, few existing work has established the connection between AIGI detection and artifact correction. To fill this gap, we propose GenShield, a unified autoregressive framework that jointly performs explainable AIGI detection and controllable artifact correction in a closed loop from diagnosis to restoration, revealing a mutually reinforcing relationship between these two tasks. We further introduce a Visual Chain-of-Thought based curriculum learning strategy that enables self-explained, multi-step ``diagnose-then-repair'' correction with an explicit stopping criterion. A high-quality dataset with large-scale ``artifact-restored'' pairs is also constructed alongside a unified evaluation pipeline. Extensive experiments on our correction benchmark and mainstream AIGI detection benchmarks demonstrate state-of-the-art performance and strong generalization of our method. The code is available at https://github.com/zhipeixu/GenShield.

Problem

• AI-generated images
• artifact correction
• image authenticity
• diffusion-based synthesis
• digital forensics

Innovation

• AI-generated image detection
• artifact correction
• unified framework
• Visual Chain-of-Thought
• autoregressive model