Climate disinformation has become a major challenge in today digital world, especially with the rise of misleading images and videos shared widely on social media. These false claims are often convincing and difficult to detect, which can delay actions on climate change. While vision-language models (VLMs) have been used to identify visual disinformation, they rely only on the knowledge available at the time of training. This limits their ability to reason about recent events or updates. The main goal of this paper is to overcome that limitation by combining VLMs with external knowledge. By retrieving up-to-date information such as reverse image results, online fact-checks, and trusted expert content, the system can better assess whether an image and its claim are accurate, misleading, false, or unverifiable. This approach improves the model ability to handle real-world climate disinformation and supports efforts to protect public understanding of science in a rapidly changing information landscape.

翻译：气候虚假信息已成为当今数字世界面临的主要挑战，尤其在社交媒体上广泛传播的误导性图像和视频日益增多的情况下。这些虚假主张往往具有说服力且难以识别，可能延缓应对气候变化的行动。尽管视觉-语言模型（VLMs）已被用于识别视觉虚假信息，但其仅依赖训练时获取的知识，这限制了模型对近期事件或动态信息的推理能力。本文的核心目标是通过将VLMs与外部知识相结合来突破这一局限。通过检索反向图像搜索结果、在线事实核查数据及可信专家内容等最新信息，该系统能更准确地评估图像及其相关主张的真实性，判断其属于准确信息、误导性内容、虚假陈述还是无法验证的信息。该方法提升了模型处理现实世界气候虚假信息的能力，并为在快速变化的信息环境中维护公众科学认知提供了技术支持。