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Filters: Author is Wu, Qingyao  [Clear All Filters]
2023-06-23
Ke, Zehui, Huang, Hailiang, Liang, Yingwei, Ding, Yi, Cheng, Xin, Wu, Qingyao.  2022.  Robust Video watermarking based on deep neural network and curriculum learning. 2022 IEEE International Conference on e-Business Engineering (ICEBE). :80–85.

With the rapid development of multimedia and short video, there is a growing concern for video copyright protection. Some work has been proposed to add some copyright or fingerprint information to the video to trace the source of the video when it is stolen and protect video copyright. This paper proposes a video watermarking method based on a deep neural network and curriculum learning for watermarking of sliced videos. The first frame of the segmented video is perturbed by an encoder network, which is invisible and can be distinguished by the decoder network. Our model is trained and tested on an online educational video dataset consisting of 2000 different video clips. Experimental results show that our method can successfully discriminate most watermarked and non-watermarked videos with low visual disturbance, which can be achieved even under a relatively high video compression rate(H.264 video compress with CRF 32).