  Digital image watermarking, which is a technique for invisibly embedding
information into an image, is used in fields such as property rights
protection. In recent years, some research has proposed the use of neural
networks to add watermarks to natural images. We take StegaStamp as an example
for our research. Whether facing traditional image editing methods, such as
brightness, contrast, saturation adjustment, or style change like 1-bit
conversion, GAN, StegaStamp has robustness far beyond traditional watermarking
techniques, but it still has two drawbacks: it is vulnerable to cropping and is
hard to train. We found that the causes of vulnerability to cropping is not the
loss of information on the edge, but the movement of watermark position. By
explicitly introducing the perturbation of cropping into the training, the
cropping resistance is significantly improved. For the problem of difficult
training, we introduce instance normalization to solve the vanishing gradient,
set losses' weights as learnable parameters to reduce the number of
hyperparameters, and use sigmoid to restrict pixel values of the generated
image.
