Imperceptible Audio Watermarking with Local Invariant Points and Adaptive Embedding Strength

WU Shiqiang; GUAN Hu; LIU Jie; ZENG Zhi; HUANG Ying; ZHANG Shuwu

doi:10.23919/cje.2023.00.356

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Shiqiang WU, Hu GUAN, Jie LIU, et al., “Imperceptible Audio Watermarking with Local Invariant Points and Adaptive Embedding Strength,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–15, xxxx doi: 10.23919/cje.2023.00.356

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Shiqiang WU, Hu GUAN, Jie LIU, et al., “Imperceptible Audio Watermarking with Local Invariant Points and Adaptive Embedding Strength,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–15, xxxx doi: 10.23919/cje.2023.00.356

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Imperceptible Audio Watermarking with Local Invariant Points and Adaptive Embedding Strength

doi: 10.23919/cje.2023.00.356

WU Shiqiang^{1, 2
,},
GUAN Hu^1
,,
LIU Jie^3
,,
ZENG Zhi^3
,,
HUANG Ying^{3
,
,},
ZHANG Shuwu^3
,

1.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
3.
Beijing University of Posts and Telecommunications, Beijing 100876, China

More Information

Author Bio:
Shiqiang WU received the B.S. degree in Electrial Engineering from the University of Science and Technology of China, Hefei, China, in 2015. And he is working toward the Ph.D. degree in Pattern Recognition and Intelligent System with the University of Chinese Academy of Sciences and Institute of Automation, Chinese Academy of Sciences. His research interests include audio watermarking, data hiding, signal processing, etc. (Email: wushiqiang2018@ia.ac.cn)

Hu GUAN received the Ph.D. degree in Digital Copyright Protection and Tracking from the University of Chinese Academy of Sciences, Beijing, China, in 2014. He is currently a research associate with the Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China. His major research areas include digital media content copyright protection and tracking, digital copyright value evaluation and copyright transactions, etc. (Email: hu.guan@ia.ac.cn)

Jie LIU is currently an associate professor in the Beijing University of Posts and Telecommunications. He received the Ph.D. degree in Pattern Recognition and Intelligent Systems from Institute of Automation, Chinese Academy of Sciences. His research interests include pattern recognition, deep learning, image processing and especially the applications to scene text detection and recognition. (Email: jie.liu@ia.ac.cn)

Zhi ZENG received the B.S. and M.S. degree in Computer Science in 2003 and 2006, respectively, both from Chongqing University (CQU), China, and the Ph.D. degree in Pattern Recognition from the Institute of Automation, Chinese Academy of Sciences (CASIA), in 2009. He is currently an associate professor in the Beijing University of Posts and Telecommunications. His research interests include 3D vision, pattern recognition, and information retrieval. (Email: zhi.zeng@bupt.edu.cn)

Ying HUANG received the M.S. degree in Software Engineering in 2016 and the Ph.D. degree in Computer Application Technology in 2019 from Taiyuan University of Technology, Taiyuan, China. She is currently a lecturer with the Beijing University of Posts and Telecommunications, Beijing. Her research areas include digital watermarking, AIGC forensics, multimedia information security, etc. (Email: ying.huang@bupt.edu.cn)

Shuwu ZHANG received the Ph.D. degree in Pattern Recognition and Intelligent System from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 1997. He is a processor with the Beijing University of Posts and Telecommunications, Beijing. His research areas include digital rights management, computer vision, cross-media content analysis, speech and language information processing, etc. (Email: shuwu.zhang@bupt.edu.cn)
Corresponding author: Email: ying.huang@bupt.edu.cn
Received Date: 2023-11-09
Accepted Date: 2024-05-11

Available Online: 2024-06-15

Abstract

Abstract

Audio watermarking is a promising technique for copyright protection of audio data. The existing audio watermarking algorithms cannot satisfy requirements on imperceptibility, embedding capacity, and robustness, especially against desynchronization attacks, such as cropping, jittering, and time-scale modification. This paper proposes a novel audio watermarking algorithm, LIPAS, based on local invariant points and adaptive embedding strength. We consider one feature robust to desynchronization attacks, i.e., local invariant points, and use these invariant points as positional references for the embedding regions. An adaptive embedding strength strategy is proposed to enhance the imperceptibility of the watermark and ensure robustness. The watermarks are embedded into the audio vectors using a polarity adjustment method. The effectiveness, imperceptibility, and robustness of the LIPAS algorithm were demonstrated in the experiments.
- Audio watermarking,
- Spread spectrum,
- Desynchronization attacks

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References(48)

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