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

• URL: http://arxiv.org/abs/2406.08772v2
• Date: Wed, 21 Aug 2024 05:00:04 GMT
• Title: MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs
• Authors: Xuannan Liu, Zekun Li, Peipei Li, Shuhan Xia, Xing Cui, Linzhi Huang, Huaibo Huang, Weihong Deng, Zhaofeng He,
• Abstract summary: multimodal misinformation detection methods often assume a single source and type of forgery for each sample. The lack of a benchmark for mixed-source misinformation has hindered progress in this field. We introduce MMFakeBench, the first comprehensive benchmark for mixed-source MMD.
• Score: 47.353720361676004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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