  Existing high-resolution satellite image forgery localization methods rely on
patch-based or downsampling-based training. Both of these training methods have
major drawbacks, such as inaccurate boundaries between pristine and forged
regions, the generation of unwanted artifacts, etc. To tackle the
aforementioned challenges, inspired by the high-resolution image segmentation
literature, we propose a novel model called HRFNet to enable satellite image
forgery localization effectively. Specifically, equipped with shallow and deep
branches, our model can successfully integrate RGB and resampling features in
both global and local manners to localize forgery more accurately. We perform
various experiments to demonstrate that our method achieves the best
performance, while the memory requirement and processing speed are not
compromised compared to existing methods.
