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In this paper, we address a new image forensics task, namely the detection offake flood images generated by ClimateGAN architecture. We do so by proposing ahybrid deep learning architecture including both a detection and a localizationbranch, the latter being devoted to the identification of the image regionsmanipulated by ClimateGAN. Even if our goal is the detection of fake floodimages, in fact, we found that adding a localization branch helps the networkto focus on the most relevant image regions with significant improvements interms of generalization capabilities and robustness against image processingoperations. The good performance of the proposed architecture is validated ontwo datasets of pristine flood images downloaded from the internet and threedatasets of fake flood images generated by ClimateGAN starting from a large setof diverse street images.

An Architecture for the detection of GAN-generated Flood Images with Localization Capabilities