MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs
<|reference_start|>MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs: 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.<|reference_end|>
@article{liu2024mmfakebench:,
    title={MMFakeBench: A Mixed-Source Multimodal Misinformation Detection
  Benchmark for LVLMs},
    author={Xuannan Liu and Zekun Li and Peipei Li and Shuhan Xia and Xing Cui and
  Linzhi Huang and Huaibo Huang and Weihong Deng and Zhaofeng He},
    journal={arXiv preprint arXiv:2406.08772},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08772},
    primaryClass={cs.CV cs.CL}
}