AI 中文总结
针对尼泊尔语OOC虚假信息检测，构建首个尼泊尔语多语言OOC基准NepOOC，评估多模态等架构发现仅文本mBERT性能最优，数据集扩充比架构优化更有效。
AI 中文摘要
脱离上下文（OOC）虚假信息将真实图像与误导性标题配对，在不篡改图像的情况下构建虚假叙事，因此检测该类信息是多模态对齐问题而非图像取证问题。尽管OOC虚假信息在尼泊尔普遍存在且后果严重，但目前尚无公开的尼泊尔语基准。我们推出NepOOC，首个公开的以尼泊尔语为主的多语言OOC基准，包含1090组图像-标题对（545组原始、545组OOC），按五类类型（编造、标题误配、时间不匹配、地理不匹配、身份不匹配）标注，标注者间一致性kappa值为0.84。对五种多模态架构及仅文本、仅图像基线的系统评估显示，在当前数据集规模下，标题语义足以实现良好性能：仅文本的mBERT模型达到94.65±0.20%的宏F1值，与最优多模态系统（ResNet-50+mBERT，94.65±0.20%）在统计上等价（McNemar中位数p=1.000，5个种子中0个在α=0.05水平显著）；仅图像模型的表现接近随机（33%-50%），且训练规模扩展表明，数据集扩充是比架构精细化或区域专业化更直接的进步路径。
英文摘要
Out-of-context (OOC) misinformation pairs authentic images with misleading captions to construct false narratives without image manipulation, making detection a problem of multimodal alignment rather than image forensics. Despite the prevalence and consequences of OOC misinformation in Nepal, no public benchmark exists for Nepali. We introduce NepOOC, the first publicly available Nepali-dominant multilingual OOC benchmark, comprising 1,090 image-caption pairs (545 pristine, 545 OOC) annotated across five typologies (fabricated, miscaptioned, temporal mismatch, geographic mismatch, identity mismatch) with inter-annotator agreement kappa = 0.84. Systematic evaluation of five multimodal architectures alongside text-only and image-only baselines reveals that caption semantics appear sufficient for strong performance at the current dataset scale. A text-only mBERT model achieves 94.65+/-0.20% Macro-F1, statistically equivalent to the best multimodal system (ResNet-50+mBERT, 94.65+/-0.20%; McNemar median p = 1.000, 0/5 seeds significant at alpha = 0.05). Image-only models perform near chance (33-50%), while training-size scaling suggests that dataset expansion is a more direct path to progress than architectural sophistication or regional specialisation.
Comments12 pages, 5 figures
