Graph Fourier Transform Based Audio Zero-Watermarking

Xu, Longting; Huang, Daiyu; Zaidi, Syed Faham Ali; Rauf, Abdul; Das, Rohan Kumar

doi:10.1109/lsp.2021.3112338

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…From the experimental results, we can find that the NC values in our scheme can reach more than 99% when resisting common attacks. So the robustness of proposed scheme and above two schemes [15,16] is comparable.…”

Section: Introductionmentioning

confidence: 84%

“…L.Xu et al [15] propose an audio zero-watermarking method based on sparse representation, the OMP algorithm and K-SVD algorithm are adopted. After that, authors propose a novel zero-watermarking technique based on the GFT [16]. They use the combined shift operator to construct the graph signal, and then the stable graph Fourier coefficients are selected for encoding.…”

Section: Introductionmentioning

confidence: 99%

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A robust zero-watermarking scheme based on non-negative matrix factorization for audio protection

Guo

Huang

2022

PLoS ONE

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The copyright problem of digital products is becoming more and more prominent. In this case, digital watermarking technology has attracted the attention of many experts and scholars in the field of information security. Among the proposed technologies, zero-watermarking technology has been favored greatly with its excellent imperceptibility. In this paper, a novel robust audio zero-watermarking scheme is designed by applying non-negative matrix decomposition algorithm to zero-watermarking technology. Firstly, the proposed scheme divides the input audio signal into fixed frames, then applies fast Fourier transform(FFT) and non-negative matrix factorization(NMF) algorithm to extract the feature vector of the original audio signal. Finally, XOR the feature vector and the digital watermark sequence to achieve the embedding of zero-watermarking. The experimental results show that the proposed scheme performs more effectively in resisting common and frame-desynchronization attacks than the existing zero-watermarking schemes.

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Section: Introductionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

A robust zero-watermarking scheme based on non-negative matrix factorization for audio protection

Guo

Huang

2022

PLoS ONE

Self Cite

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“…The specific attack types include the following several ways: Table 3 presents the BER and NC values of watermarks extracted by the proposed and related zero-watermarking schemes when resisting desynchronization attacks with 64×64 watermark images. Meanwhile, Figures 4-8, respectively, exhibit the 64×64 watermark images extracted by the proposed scheme, DCT-DWT-SVD scheme, K-SVD scheme [9], GFT-K-means scheme [10] and NMF scheme [14] against four types of desynchronization attacks. According to Table 3, the proposed scheme performs well against all four attack types, especially against TSM attacks, which is better than the other three schemes.…”

Section: Robust Performance Analysis Against Desynchronization Attacksmentioning

confidence: 99%

A zero‐watermarking technique based on i‐vector model for audio copyright protection

Xu,

He,

Guo

2023

Electronics Letters

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Audio zero‐watermarking is a promising technology for audio copyright protection. It does not modify the content of the original carrier and has good imperceptibility. Considering that i‐vector model can map audio signals of different lengths into a vector space of fixed length, which has good robustness against desynchronization attacks, a robust audio zero‐watermarking technique based on i‐vector model is proposed here. The proposed scheme extracts the i‐vector of the input signal to identify its inherent stability features and then generates a key by XOR operation on the watermark and encoded feature sequences. Experimental results show that this scheme can effectively resist common attacks and desynchronization attacks.

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“…The first approaches were proposed to protect multimedia data. Among techniques for multimedia data, some has been specialized on copyright protection and tamper detection on video [38]- [40], audio [41], [42] and images [43], [44]. Later some approaches oriented to protect documents [45], [46] or textual content stored in relational databases [1], [47] emerged.…”

Section: Related Workmentioning

confidence: 99%

Reducing Multiple Occurrences of Meta-Mark Selection in Relational Data Watermarking

2022

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Contrary to multimedia data watermarking approaches, it is not recommended that relational data watermarking techniques consider sequential selection for marks in the watermark and embedding locations in the digital asset being protected. Indeed, considering the database relations' elements, i.e., tuple and attributes, when watermarking techniques are based on sequential processes, watermark detection can be easily compromised by performing subset reverse order attacks. As a result, attackers can obtain owner evidence-free high-quality data since no data modifications for mark removing are required for the malicious operation to succeed. A standard solution to this problem has been pseudo-random selection, which however often leads to choosing the same marks multiple times, ignoring others, thus compromising the embedding of the entire watermark. This work proposes an engine that contributes to control marks' recurrent selection, allowing marks excluded by previous approaches to be considered. The experiments performed show a dramatic improvement of the embedded watermark quality when the proposed engine is included in watermarking techniques' architecture. They also provide evidence that this proposal leads to higher resilience against common malicious operations such as subset and superset attacks.

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Graph Fourier Transform Based Audio Zero-Watermarking

Cited by 11 publications

References 26 publications

A robust zero-watermarking scheme based on non-negative matrix factorization for audio protection

A robust zero-watermarking scheme based on non-negative matrix factorization for audio protection

A zero‐watermarking technique based on i‐vector model for audio copyright protection

Reducing Multiple Occurrences of Meta-Mark Selection in Relational Data Watermarking

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