{"type":"ScholarlyArticle","title":"Fast and Generalizable AI-Generated Image Detection via Model-Agnostic Feature Reconstruction","authors":[{"name":"Qinghui He","slug":"qinghui-he","given":"Qinghui","family":"He","affiliation":""},{"name":"Haifeng Zhang","slug":"haifeng-zhang","given":"Haifeng","family":"Zhang","affiliation":""},{"name":"Bo Liu","slug":"bo-liu","given":"Bo","family":"Liu","affiliation":""},{"name":"Yang Wei","slug":"yang-wei","given":"Yang","family":"Wei","affiliation":""}],"abstract":"Generative models continue to advance rapidly in both fidelity and diversity, posing increasing challenges for reliably distinguishing generated images from real ones. Existing reconstruction-based detection methods often rely on assumptions tied to specific generative models, which leads to limited cross-model generalization and substantial computational overhead. In this paper, we propose General Feature Reconstruction Error (GFRE), a fast and generalizable detection paradigm that leverages reconstruction behavior in a general-purpose representation space. Our key insight is that real and generated images exhibit consistently different reconstruction stability when projected into universal visual representations. Instead of tracing generator-specific artifacts, GFRE employs a lightweight autoencoder to model the reconstructability of image representations, producing a reconstruction signal that is inherently generator-agnostic and transferable across diverse generative processes. Extensive experiments on images synthesized by 18 different generative models demonstrate that GFRE consistently outperforms existing state-of-the-art methods, achieving a 4.70% improvement in detection accuracy and an 8.20% gain in cross-model generalization. Moreover, by avoiding costly generator inversion and diffusion-based reconstruction, GFRE reduces reconstruction error extraction time by up to 150x, enabling efficient and scalable deployment.","doi":"10.24963/ijcai.2026/131","url":"https://ijcai.org/proceedings/2026/131","pdf":"https://ijcai.org/proceedings/2026/0131.pdf","markdown":"https://ijcai.org/proceedings/2026/131.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/131","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":131,"pages":{"first":1170,"last":1178},"keywords":["Computer Vision: Recognition (object detection, categorization)"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}