  With the development of social media, rumors have been spread broadly on
social media platforms, causing great harm to society. Beside textual
information, many rumors also use manipulated images or conceal textual
information within images to deceive people and avoid being detected, making
multimodal rumor detection be a critical problem. The majority of multimodal
rumor detection methods mainly concentrate on extracting features of source
claims and their corresponding images, while ignoring the comments of rumors
and their propagation structures. These comments and structures imply the
wisdom of crowds and are proved to be crucial to debunk rumors. Moreover, these
methods usually only extract visual features in a basic manner, seldom consider
tampering or textual information in images. Therefore, in this study, we
propose a novel Vision and Graph Fused Attention Network (VGA) for rumor
detection to utilize propagation structures among posts so as to obtain the
crowd opinions and further explore visual tampering features, as well as the
textual information hidden in images. We conduct extensive experiments on three
datasets, demonstrating that VGA can effectively detect multimodal rumors and
outperform state-of-the-art methods significantly.
