SalArt-VQA: Diagnosing Whether VLMs Understand Salient Artifacts in Generated Images
SalArt-VQA is a diagnostic benchmark for evaluating fine-grained understanding of artifacts in AI-generated images by vision-language models (VLMs). It includes 950 images and 3,681 multiple-choice questions covering presence detection, semantic localization, spatial grounding, and evidence-grounded defect identification. Testing 20 VLMs revealed that even the best model, with 99.37% detection recall, answered all four artifact questions correctly on only 53.26% of images, highlighting a sensitivity-calibration tradeoff.
[2606.12671] SalArt-VQA: Diagnosing Whether VLMs Understand Salient Artifacts in Generated Images
[Submitted on 10 Jun 2026]
Title:SalArt-VQA: Diagnosing Whether VLMs Understand Salient Artifacts in Generated Images
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Abstract:Vision-language models (VLMs) are increasingly used to detect whether AI-generated images contain visible artifacts, yet their ability to analyze such artifacts remains poorly understood. A correct image-level decision can still hide important failures: a model may correctly flag an artifact while relying on the wrong visual cue, selecting the wrong region, or describing a defect that the image does not support. To evaluate these behaviors directly, we introduce SalArt-VQA, a diagnostic benchmark for fine-grained SALient ARTifact understanding in AI-generated images. SalArt-VQA contains 950 images and 3,681 human-authored multiple-choice questions spanning artifact images, matched real reference images, and paired generated reference images. Four aligned question types evaluate presence detection, semantic localization, spatial grounding, and evidence-grounded defect identification, while the reference splits test calibration and abstention when the annotated defect is absent. Across 20 VLMs, SalArt-VQA reveals failures that image-level detection accuracy hides: the strongest model reaches 99.37% detection recall on artifact images but answers all four artifact-side questions correctly on only 53.26% of images. Comparing artifact images with artifact-free references reveals a sensitivity-calibration tradeoff: sensitive models often make unsupported artifact claims, while conservative models avoid false alarms largely by missing real artifacts. These results show that high artifact detection accuracy alone does not imply grounded artifact understanding. SalArt-VQA exposes these hidden failure modes and provides a fine-grained evaluation of whether VLM artifact claims are supported by local visual evidence.
Comments: 23 pages, 7 figures, 7 tables. Dataset: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.12671 [cs.CV]
(or arXiv:2606.12671v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.12671
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xiaoxiao Sun [view email] [v1] Wed, 10 Jun 2026 20:55:50 UTC (9,260 KB)
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