Synthesizing Artifact Dataset for Pixel-level Detection

• URL: http://arxiv.org/abs/2509.19589v1
• Date: Tue, 23 Sep 2025 21:28:33 GMT
• Title: Synthesizing Artifact Dataset for Pixel-level Detection
• Authors: Dennis Menn, Feng Liang, Diana Marculescu,
• Abstract summary: Artifact detectors enhance the performance of image-generative models by serving as reward models during fine-tuning.<n>We propose an artifact corruption pipeline that automatically injects artifacts into clean, high-quality synthetic images on a predetermined region.<n>The proposed method achieves performance improvements of 13.2% for ConvNeXt and 3.7% for Swin-T, as verified on human-labeled data.
• Score: 16.31703475992344
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Artifact detectors have been shown to enhance the performance of image-generative models by serving as reward models during fine-tuning. These detectors enable the generative model to improve overall output fidelity and aesthetics. However, training the artifact detector requires expensive pixel-level human annotations that specify the artifact regions. The lack of annotated data limits the performance of the artifact detector. A naive pseudo-labeling approach-training a weak detector and using it to annotate unlabeled images-suffers from noisy labels, resulting in poor performance. To address this, we propose an artifact corruption pipeline that automatically injects artifacts into clean, high-quality synthetic images on a predetermined region, thereby producing pixel-level annotations without manual labeling. The proposed method enables training of an artifact detector that achieves performance improvements of 13.2% for ConvNeXt and 3.7% for Swin-T, as verified on human-labeled data, compared to baseline approaches. This work represents an initial step toward scalable pixel-level artifact annotation datasets that integrate world knowledge into artifact detection.

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