4.7 Article

An efficient video watermark method using blockchain

期刊

KNOWLEDGE-BASED SYSTEMS
卷 259, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.knosys.2022.110066

关键词

Video watermark; Optimization; Distortion; Blockchain; Robustness

向作者/读者索取更多资源

This paper proposes a blockchain-based approach for video watermarking, which selects wavelet coefficient blocks in key frames and leverages blockchain to connect them. It solves the issues of storing a large number of coordinates and vulnerability to video frame attacks in existing solutions. Experimental results demonstrate that this approach achieves high storage efficiency and significantly improves overall robustness, making novel and significant contributions to the field of video watermarking.
Nowadays, one of the most common approaches for video watermarking is to use singular value decomposition in the discrete wavelet transform domain. In our prior work, we formulated a video watermarking problem to achieve the maximum peak signal to noise ratio as an optimization problem. It chooses wavelet coefficient points in key frames with minimum distortion cost. Yet the existing solution has two drawbacks (1) it requires to store a large number of coordinates depending on the watermark size; (2) it is vulnerable to video frame attacks on the video frame texture area. To avoid them, this paper proposes a blockchain-based approach. Its main concept is to choose wavelet coefficient blocks in key frames and leverage blockchain to connect all the blocks. Our experimental results show that this approach is memory efficient because it only requires to store a single key for a key frame, which is independent of the watermark size; and the overall robustness is greatly improved due to the randomness of the hash function used in the blockchain. Hence, this work had made novel and significant contributions to the field of video watermarking. (c) 2022 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据