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arXiv 2104.05893cs.CVcs.CL
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NewsCLIPpings:上下文外多模态媒体的自动生成

NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media

University of California, Berkeley(加州大学伯克利分校)

Grace Luo, Trevor Darrell, Anna Rohrbach

摘要:

在线错误信息是一个普遍的社会问题,对手依赖从廉价假货到复杂的深度假货的各种工具。我们被威胁场景所激励,在这种场景中,一个图像被断章取义地用来支持某种叙述。虽然一些用于检测图像-文本不一致的先前数据集通过文本操作生成样本,但我们提出了一个图像和文本都未被操作但不匹配的数据集。我们介绍了几种策略,自动检索令人信服的图像为一个给定的标题,捕捉不一致的实体或语义上下文的情况下。我们的大规模自动生成的NewsCLIPpings数据集:(1)表明机器驱动的图像再利用现在是一个现实的威胁,(2)提供的样本代表了新闻中文本和图像之间不匹配的挑战性实例,这些实例能够误导人类。我们在我们的数据集上对几种最先进的多模态模型进行了基准测试,并分析了它们在不同预训练域和视觉骨干中的性能。

英文摘要:

Online misinformation is a prevalent societal issue, with adversaries relying on tools ranging from cheap fakes to sophisticated deep fakes. We are motivated by the threat scenario where an image is used out of context to support a certain narrative. While some prior datasets for detecting image-text inconsistency generate samples via text manipulation, we propose a dataset where both image and text are unmanipulated but mismatched. We introduce several strategies for automatically retrieving convincing images for a given caption, capturing cases with inconsistent entities or semantic context. Our large-scale automatically generated NewsCLIPpings Dataset: (1) demonstrates that machine-driven image repurposing is now a realistic threat, and (2) provides samples that represent challenging instances of mismatch between text and image in news that are able to mislead humans. We benchmark several state-of-the-art multimodal models on our dataset and analyze their performance across different pretraining domains and visual backbones.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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