ItoV: Efficiently Adapting Deep Learning-Based Image Watermarking to Video Watermarking

Guanhui Ye; Jiashi Gao; Yuchen Wang; Liyan Song; Xuetao Wei

10.1109/CoST60524.2023.00047

2023

2023 International Conference on Culture-Oriented Science and Technology (CoST)

979-8-3503-5800-1

2023 International Conference on Culture-Oriented Science and Technology (CoST)

192-197

11-14 Oct. 2023

Xi’an, China

Robust watermarking tries to conceal information within a cover imag e/video imperceptibly that is resistant to various distortions. Recently, deep learning-based approaches for image watermarking have made significant advancements in robustness and invisibility. However, few studies focused on video watermarking using deep neural networks due to the high complexity and computational costs. Our paper aims to answer this research question: Can well-designed deep learning-based image watermarking be efficiently adapted to video watermarking? Our answer is positive. First, we revisit the workflow of deep learning-based watermarking methods that leads to a critical insight: temporal information in the video may be essential for general computer vision tasks but not for specific video watermarking. Inspired by this insight, we propose a method named ITOV for efficiently adapting deep learning-based Image watermarking to Video watermarking. Specifically, ITOV merges the temporal dimension of the video with the channel dimension to enable deep neural networks to treat videos as images. We further explore the effects of different convolutional blocks in video watermarking. We find that spatial convolution is the influential primary component in video watermarking, and depthwise convolutions significantly reduce computational costs with negligible impact on performance. In addition, we propose a new frame loss to constrain that the watermark intensity in each video clip frame is consistent, significantly improving the invisibility. Extensive experiments show the superior performance of the adapted video watermarking method compared with the state-of-the-art methods on Kinetics-600 and Inter4K datasets, which demonstrates the efficacy of our method ITOV.

Watermarking Video Watermarking Convolutional Neural Networks Robustness Invisibility

第一 ; 通讯

EI

20240215337975

Convolution ; Convolutional Neural Networks ; Deep Neural Networks ; Robustness (Control Systems) ; Watermarking

Ergonomics And Human Factors Engineering:461.4 ; Information Theory And Signal Processing:716.1 ; Data Processing And Image Processing:723.2 ; Control Systems:731.1 ; Papermaking Processes:811.1.1

IEEE

Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Guanhui Ye,Jiashi Gao,Yuchen Wang,et al. ItoV: Efficiently Adapting Deep Learning-Based Image Watermarking to Video Watermarking[C],2023:192-197.