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.

翻译：现有高分辨率卫星图像篡改定位方法依赖于基于分块或降采样的训练方式。这两种训练方法均存在明显缺陷，例如原始区域与伪造区域边界不精确、产生伪影等问题。为应对上述挑战，受高分辨率图像分割领域研究的启发，我们提出一种名为HRFNet的新型模型，以实现高效的卫星图像篡改定位。具体而言，通过引入浅层与深层分支，该模型能够以全局和局部方式成功融合RGB特征与重采样特征，从而更精确地定位伪造区域。我们通过大量实验证明，所提方法在保持与现有方法相当的内存需求与处理速度的前提下，实现了最佳性能。