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Digital image watermarking, which is a technique for invisibly embeddinginformation into an image, is used in fields such as property rightsprotection. In recent years, some research has proposed the use of neuralnetworks to add watermarks to natural images. We take StegaStamp as an examplefor our research. Whether facing traditional image editing methods, such asbrightness, contrast, saturation adjustment, or style change like 1-bitconversion, GAN, StegaStamp has robustness far beyond traditional watermarkingtechniques, but it still has two drawbacks: it is vulnerable to cropping and ishard to train. We found that the causes of vulnerability to cropping is not theloss of information on the edge, but the movement of watermark position. Byexplicitly introducing the perturbation of cropping into the training, thecropping resistance is significantly improved. For the problem of difficulttraining, we introduce instance normalization to solve the vanishing gradient,set losses' weights as learnable parameters to reduce the number ofhyperparameters, and use sigmoid to restrict pixel values of the generatedimage.

CropDefender: deep watermark which is more convenient to train and more robust against cropping