Zero-watermarking Algorithm for Audio and Video Matching Verification

Wenxue Sun; Huiyuan Zhao; Xiao Zhang; Yuchao Sun; Xiaoxin Liu; Xueling Lv; Di Fan; Wenxue Sun; Huiyuan Zhao; Xiao Zhang; Yuchao Sun; Xiaoxin Liu; Xueling Lv; Di Fan

doi:10.3934/math.2022468

AIMS Mathematics

2022, Volume 7, Issue 5: 8390-8407. doi: 10.3934/math.2022468

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Research article Special Issues

Zero-watermarking Algorithm for Audio and Video Matching Verification

1.
Shandong University of Science and Technology, Qingdao, 266590, Shandong, China
2.
Department of Management and Economics, Tianjin University, Tianjin, 300072, China

Received: 04 November 2021 Revised: 26 January 2022 Accepted: 06 February 2022 Published: 28 February 2022
MSC : 00A69

For the needs of tamper-proof detection and copyright identification of audio and video matching, this paper proposes a zero-watermark algorithm that can be used for audio and video matching verification. The algorithm segments audio and video in smaller time units, generates a video frame feature matrix based on NSCT, DCT, and SVD, and generates a sound watermark based on methods such as DWT and K-means. The zero watermark combines video, audio and copyright information. The experimental results show that the zero watermark generated by this algorithm can not only realize highly accurate matching detection and positioning of audio and video, but also well resist common single attack and combination attacks such as noise, scaling, rotation, frame attack and format conversion, which has good robustness.
- audio and video matching verification,
- zero watermark,
- copyright recognition,
- robustness
Citation: Wenxue Sun, Huiyuan Zhao, Xiao Zhang, Yuchao Sun, Xiaoxin Liu, Xueling Lv, Di Fan. Zero-watermarking Algorithm for Audio and Video Matching Verification[J]. AIMS Mathematics, 2022, 7(5): 8390-8407. doi: 10.3934/math.2022468

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Abstract

For the needs of tamper-proof detection and copyright identification of audio and video matching, this paper proposes a zero-watermark algorithm that can be used for audio and video matching verification. The algorithm segments audio and video in smaller time units, generates a video frame feature matrix based on NSCT, DCT, and SVD, and generates a sound watermark based on methods such as DWT and K-means. The zero watermark combines video, audio and copyright information. The experimental results show that the zero watermark generated by this algorithm can not only realize highly accurate matching detection and positioning of audio and video, but also well resist common single attack and combination attacks such as noise, scaling, rotation, frame attack and format conversion, which has good robustness.

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