A novel anti-collusion audio fingerprinting scheme based on fourier coefficients reversing

Li, Ming, Chang, Huimin, Xiang, Yong and An, Dezhi 2020, A novel anti-collusion audio fingerprinting scheme based on fourier coefficients reversing, IEEE Signal Processing Letters, vol. 27, pp. 1794-1798, doi: 10.1109/lsp.2020.3028037.

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Title A novel anti-collusion audio fingerprinting scheme based on fourier coefficients reversing
Author(s) Li, Ming
Chang, Huimin
Xiang, YongORCID iD for Xiang, Yong orcid.org/0000-0003-3545-7863
An, Dezhi
Journal name IEEE Signal Processing Letters
Volume number 27
Start page 1794
End page 1798
Total pages 5
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2020
ISSN 1070-9908
Summary Most anti-collusion audio fingerprinting schemes are aiming at finding colluders from the illegal redistributed audio copies. However, the loss caused by the redistributed versions is inevitable. In this letter, a novel fingerprinting scheme is proposed to eliminate the motivation of collusion attack. The audio signal is transformed to the frequency domain by the Fourier transform, and the coefficients in frequency domain are reversed in different degrees according to the fingerprint sequence. Different from other fingerprinting schemes, the coefficients of the host media are excessively modified by the proposed method in order to reduce the quality of the colluded version significantly, but the imperceptibility is well preserved. Experiments show that the colluded audio cannot be reused because of the poor quality. In addition, the proposed method can also resist other common attacks. Various kinds of copyright risks and losses caused by the illegal redistribution are effectively avoided, which is significant for protecting the copyright of audio.
Language eng
DOI 10.1109/lsp.2020.3028037
Indigenous content off
Field of Research 0801 Artificial Intelligence and Image Processing
0906 Electrical and Electronic Engineering
1005 Communications Technologies
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30144801

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