Current multimodal misinformation detection (MMD) methods often assume a
single source and type of forgery for each sample, which is insufficient for
real-world scenarios where multiple forgery sources coexist. The lack of a
benchmark for mixed-source misinformation has hindered progress in this field.
To address this, we introduce MMFakeBench, the first comprehensive benchmark
for mixed-source MMD. MMFakeBench includes 3 critical sources: textual veracity
distortion, visual veracity distortion, and cross-modal consistency distortion,
along with 12 sub-categories of misinformation forgery types. We further
conduct an extensive evaluation of 6 prevalent detection methods and 15 large
vision-language models (LVLMs) on MMFakeBench under a zero-shot setting. The
results indicate that current methods struggle under this challenging and
realistic mixed-source MMD setting. Additionally, we propose an innovative
unified framework, which integrates rationales, actions, and tool-use
capabilities of LVLM agents, significantly enhancing accuracy and
generalization. We believe this study will catalyze future research into more
realistic mixed-source multimodal misinformation and provide a fair evaluation
of misinformation detection methods.

当前的多模态误信息检测（MMD）方法通常假设每个样本只有一个来源和一种伪造类型，这对于存在多个伪造来源的现实场景是不足够的。缺乏混合来源误信息的基准已经妨碍了这个领域的进展。为了解决这个问题，我们引入了 MMFakeBench，这是第一个全面的混合来源 MMD 基准。MMFakeBench 包括文本真实性失真、视觉真实性失真和跨模态一致性失真这三个关键来源，以及 12 个不同类别的误信息伪造类型。我们在零 - shot 设置下对 MMFakeBench 进行了 6 种流行的检测方法和 15 个大规模视觉语言模型（LVLMs）的广泛评估。结果表明，当前的方法在这个具有挑战性和现实的混合来源 MMD 环境下表现不佳。此外，我们提出了一种创新的统一框架，将 LVLM 智能体的逻辑、行动和工具使用能力相结合，显著提高了准确性和概括能力。我们相信这项研究将推动未来对更加真实的混合来源多模态误信息的研究，并提供对误信息检测方法的公正评估。

MMFakeBench: 用于 LVLM 的混合源多模态虚假信息检测基准

MMFakeBench: A Mixed-Source Multimodal Misinformation Detection  Benchmark for LVLMs

With the expansion of social media and the increasing dissemination of
multimedia content, the spread of misinformation has become a major concern.
This necessitates effective strategies for multimodal misinformation detection
(MMD) that detect whether the combination of an image and its accompanying text
could mislead or misinform. Due to the data-intensive nature of deep neural
networks and the labor-intensive process of manual annotation, researchers have
been exploring various methods for automatically generating synthetic
multimodal misinformation - which we refer to as Synthetic Misinformers - in
order to train MMD models. However, limited evaluation on real-world
misinformation and a lack of comparisons with other Synthetic Misinformers
makes difficult to assess progress in the field. To address this, we perform a
comparative study on existing and new Synthetic Misinformers that involves (1)
out-of-context (OOC) image-caption pairs, (2) cross-modal named entity
inconsistency (NEI) as well as (3) hybrid approaches and we evaluate them
against real-world misinformation; using the COSMOS benchmark. The comparative
study showed that our proposed CLIP-based Named Entity Swapping can lead to MMD
models that surpass other OOC and NEI Misinformers in terms of multimodal
accuracy and that hybrid approaches can lead to even higher detection accuracy.
Nevertheless, after alleviating information leakage from the COSMOS evaluation
protocol, low Sensitivity scores indicate that the task is significantly more
challenging than previous studies suggested. Finally, our findings showed that
NEI-based Synthetic Misinformers tend to suffer from a unimodal bias, where
text-only MMDs can outperform multimodal ones.

本研究针对多模态虚假信息检测，提出了基于 CLIP 的命名实体交换的依据。我们提供了包括图像 - 标题不匹配 (OOC)、跨模态命名实体不一致 (NEI) 和混合方法在内的新的和现有的综合比较研究，以便评估检测模型的性能。我们的研究结果表明 CLIP-based Named Entity Swapping 可以提高模型的多模态准确性，而混合方法可以进一步提高检测准确性