In this paper, we present our system for the SHROOM-Visions shared task, which focuses on character-level hallucination localization and five-category classification in responses to image-grounded questions across four languages. We investigate a synthetic-data augmentation pipeline that combines annotator-agreement- and class-rarity-aware upsampling with controlled hallucination injection conditioned on image and response signals. We use the resulting data to fine-tune Qwen3-VL-8B-Instruct with LoRA to generate localized hallucination spans, confidence scores, and category labels as structured JSON. Conservative augmentation improves average validation IoU and Cor+Lbl over training on the original data, but outperforms the baseline on the test set only for Chinese, indicating substantial language-dependent effects. In the official Cor+Lbl ranking, our submissions placed 7th among 28 teams for English, 5th among 21 for French, 6th among 21 for Italian, and 5th among 22 for Chinese.