Abstract

Since most of the previous video watermark algorithms regard a video as a series of consecutive images, the embedding and extraction of watermark are performed on these images, and the correlation and redundancy among frames of a video are not considered. Such algorithms are weak in protecting against frame attacks. In order to improve the robustness, we take into consideration the correlation and redundancy among the frames of a video to propose a blind video watermark algorithm based on tensor decomposition. First, a grayscale video is represented as a 3-order tensor, and the core tensor is obtained by tensor decomposition. Second, the watermark embedding position is selected based on the stability of the maximum value in the core tensor because the core tensor represents the main energy of a video. Then, the watermark is embedded by quantifying the maximum value in the core tensor. Finally, the watermark is uniformly distributed across frames of a video by inverse tensor decomposition. The experiments show that our algorithm based on tensor decomposition has better imperceptibility and robustness against common video attacks.

Highlights

  • Copyright protection is more and more important as digital videos become popular

  • In order to improve the robustness, we take into consideration the correlation and redundancy among the frames of a video to propose a blind video watermark algorithm based on tensor decomposition

  • The core tensor is obtained by Tucker decomposition of the 3-order tensor

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Summary

Introduction

Copyright protection is more and more important as digital videos become popular. Video watermark technology is the digital watermark technology with video being the carrier. The spatial video watermark algorithm proposed by Hartung [3] converts the original video image into a one-dimensional signal and modulates the watermark into a pseudo-random sequence. This watermark is embedded into the one-dimensional signal This classical spatial domain algorithm has disadvantages in robustness against attacks such as video compression and filtering. In order to further improve anti-attack capability, Chandra [10] first used Singular Value Decomposition (SVD) for digital watermark in 2001, and embedded watermark image into singular value of the carrier image. The modified core tensor is uniformly distributed across frames of a video by inverse Tucker decomposition. (3) The modified core tensor is uniformly distributed among the frames of a video by inverse Tucker decomposition, so that the video quality and the imperceptibility of watermark are guaranteed.

Tensor
Tensor unfolding
Tensor decomposition
A watermark algorithm based on tensor decomposition
The process of watermark embedding
The process of extracting watermark
Metrics
Experiment parameters
Robustness test
Conclusions
Full Text
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