  In this paper, we propose a basic RGB single-mode model based on weakly
supervised training under pseudo labels, which performs high-precision
authenticity identification under multi-scene typical target remote sensing
images. Due to the imprecision of Mask generation, we divide the task into two
sub-tasks: generating pseudo-mask and fine-tuning model based on generated
Masks. In generating pseudo masks, we use MM-Fusion as the base model to
generate masks for large objects such as planes and ships. By manually
calibrating the Mask of a small object such as a car, a highly accurate
pseudo-mask is obtained. For the task of fine-tuning models based on generating
masks, we use the WSCL model as the base model. It is worth noting that due to
the difference between the generated pseudo-Masks and the real Masks, we
discard the image feature extractors such as SRM and Noiseprint++ in WSCL, and
select the unscaled original image for training alone, which greatly ensures
the match between the image and the original label. The final trained model
achieved a score of 90.7702 on the test set.
