Abstract: In recent years， spatial watermarking detection based on deep learning has achieved good results， but the detection result in transform domain is not ideal. To solve this problem， this paper proposes a watermark detection model based on DWT-SVD and transfer learning. The whole model is divided into three parts. In the embedding watermark part， the watermark image is preprocessed first， then the carrier image is processed by three-level wavelet transform and singular value decomposition， and finally the watermark embedding is completed. In the part of transfer learning， the watermarked images and the original images dataset is put into the improved neural network model VGG19-XVGG19， which is used for transfer learning training， features extraction， model parameters optimization， and detection model construction. In the watermark detection part， the model is used to detect and preprocess the image. If a watermark is detected， then DWT-SVD inverse transform is used to extract the watermark. Experimental results show that the proposed watermarking detection model in wavelet domain has short time consumption and high accuracy.

Key words: watermarking detection model, transfer learning, DWT-SVD, high detection accuracy