@inproceedings{
liu2024fkaowl,
title={{FKA}-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented {LVLM}s},
author={Xuannan Liu and Pei Pei Li and Huaibo Huang and Zekun Li and Xing Cui and jiahao.liang and lixiong Qin and Weihong Deng and Zhaofeng He},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=9UGFbtt44F}
}
The massive generation of multimodal fake news involving both text and images exhibits substantial distribution discrepancies, prompting the need for generalized detectors. However, the insulated nature of training restricts the capability of classical detectors to obtain open-world facts. While Large Vision-Language Models (LVLMs) have encoded rich world knowledge, they are not inherently tailored for combating fake news and struggle to comprehend local forgery details. In this paper, we propose FKA-Owl, a novel framework that leverages forgery-specific knowledge to augment LVLMs, enabling them to reason about manipulations effectively. The augmented forgery-specific knowledge includes semantic correlation between text and images, and artifact trace in image manipulation. To inject these two kinds of knowledge into the LVLM, we design two specialized modules to establish their representations, respectively. The encoded knowledge embeddings are then incorporated into LVLMs. Extensive experiments on the public benchmark demonstrate that FKA-Owl achieves superior cross-domain performance compared to previous methods. Code will be made publicly available.
FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs
Xuannan Liu Pei Pei Li Huaibo Huang Zekun Li Xing Cui jiahao.liang lixiong Qin Weihong Deng Zhaofeng He