{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:36:40Z","timestamp":1773801400120,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>The rapid advancement of generative models, which produce increasingly realistic synthetic images, urgently demands robust and generalizable detection methods. Consequently, research has largely pivoted to leveraging large-scale Vision Foundation Models (VFMs) for enhanced generalization. However, existing VFM-based approaches primarily adhere to either perceptual or generative paradigms, each with limitations: perceptual models capture high-level semantics but often miss subtle artifacts, whereas generative models emphasize fine-grained flaws yet overlook semantic inconsistency. To resolve this inherent trade-off, we introduce SynerDetect, a novel hierarchical synergistic framework that fundamentally unifies the two paradigms. SynerDetect achieves deep integration of heterogeneous forensic representations through two levels of synergy: Cross-Model Interactive Distillation (CMID) distills generative forensic signals into perceptual encoders via prompt-guided reconstruction; and Optimal Transport-Guided Discriminative Contrastive Learning (OT-DCL) structurally aligns and integrates these heterogeneous representations, consolidating them into a robust, unified detection space. SynerDetect achieves superior performance on standard benchmarks (AIGCDetectBenchmark and GenImage) and attains a notable 5.20% accuracy gain on the challenging Chameleon benchmark, whose synthetic images consistently pass the Visual Turing Test. These results unequivocally validate the robust, real-world generalization of our unified cross-paradigm framework.<\/jats:p>","DOI":"10.1609\/aaai.v40i8.37568","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:30:31Z","timestamp":1773790231000},"page":"6405-6414","source":"Crossref","is-referenced-by-count":0,"title":["SynerDetect: Hierarchical Synergistic Learning for Generalizable AI-Generated Image Detection"],"prefix":"10.1609","volume":"40","author":[{"given":"Shuaibo","family":"Li","sequence":"first","affiliation":[]},{"given":"Yijun","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zhaohu","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Hongqiu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Pengfei","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Xingyu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zekai","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37568\/41530","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37568\/41530","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:30:32Z","timestamp":1773790232000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37568"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i8.37568","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}