FLDCF: A Collaborative Framework for Forgery Localization and Detection in Satellite Imagery

Satellite images are highly susceptible to forgery due to various editing techniques. Traditional forgery detection methods, designed for natural images, often fail when applied to satellite images because of differences in sensing technology and processing protocols. The rise of generative models, such as diffusion models, has further complicated the detection of forgeries in satellite images. This study tackles these challenges from both methodological and data perspectives. We introduce a multitask forgery localization and detection collaborative framework (FLDCF), comprising a multiview forgery localization network (M-FLnet) and a forgery detection network. The M-FLnet, leveraging a content-based prior, generates forgery masks that serve as auxiliary information to improve the detection network's accuracy. Conversely, the detection network refines these masks, reducing noise for authentic images. Furthermore, two novel forgery datasets, namely, Fake-Vaihingen and Fake-LoveDA, are derived from the Vaihingen and LoveDA satellite image sets, respectively, by exploiting the latest generative models. These datasets represent the first open-source datasets for forgery localization and detection in remote sensing. Extensive experimental results on Fake-Vaihingen and Fake-LoveDA demonstrate that the proposed FLDCF can effectively detect sophisticated forgeries in satellite imagery. The source code and datasets in this work are available at https://github.com/littlebeen/Forgery-localization-for-remote-sensing.