Abstract: Digital watermarking technology plays an important role in multimedia protection,and the various demands for practical applications promotes the development of digital watermarking technology.Recently,the robustness of the deep learning-based watermarking model has been greatly improved,but the embedding process is mostly carried out in the spatial domain,and this causes obvious distortions to original images.In addition,existing methods do not work well under the screen-shooting attack.To solve the above problems,this paper proposes a deep learning-based DCT domain watermarking method which is robust to the screen-shooting attack.This model consists of a DCT layer,an encoder,a decoder,and a screen shoot simulation layer.The DCT layer converts the Y component of images into the DCT domain,then the encoder embeds secret messages into the image by mo-difying the DCT coefficients through end-to-end training.This embedding method in the frequency domain makes the watermark information to be distributed to the whole space of images so that the distortion effect is reduced.Furthermore,we propose a noise layer to simulate moiré and light reflection effects,which are common distortions in the screen-shooting attack.The training process is splitted into two stages.In the first stage,the encoder and decoder are trained end-to-end.While in the second stage,the screen-shooting simulation layer and traditional distortion attacks are used to augment the watermarked image,then we use the distorted watermarked image to furtheroptimize the decoder.Extensive experimental results show that the proposed model has high transparency and robustness,and is superior to other methods in screen robustness.

Key words: Digital watermark, Deep learning, DCT Transform, Imperceptibility, Screen-shooting robustness