  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
is publicly available at https://liuxuannan.github.io/FKA_Owl.github.io/.
