  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.
