Robust audio watermarking scheme based on fractional Charlier moment transform and dual tree complex wavelet transform

Yamni, Mohamed; Karmouni, Hicham; Sayyouri, Mhamed; Qjidaa, Hassan

doi:10.1016/j.eswa.2022.117325

Cited by 22 publications

(10 citation statements)

References 32 publications

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“…The performance of the proposed audio watermarking system is compared with that of six notable audio watermarking systems, each chosen for specific reasons. These systems, namely FrCT-DTCWT 19 , DWT-DTMT 10 , DWT-DCT 9 , DCT-SVD 17 , DWT 3 , DCT 5 , 16 , 18 , and SVD 7 , were selected based on considerations such as the popularity and similarity of the employed transform domains for embedding and their proven track record of robustness against common signal processing manipulations. This comparison assesses the proposed system against these benchmarks in terms of payload capacity, imperceptibility, robustness of the watermark against common signal processing manipulations, and computational complexity.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…In the watermark extraction process, these synchronization codes are firstly found, and then the watermark bits that follow the synchronization code can be extracted. Without using a synchronization code strategy, we proposed in 19 a hybrid approach robust to attacks, including shifting attacks, based on the Dual Tree Complex Wavelet Transform (DTCWT) and the Fractional Charlier Transform (FrCT). We embedded the watermark in the host signal by manipulating the coefficients resulting from the application of the DTCWT and FrCT, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…These reasons underscore the significance of fast watermark extraction in the context of audio watermarking systems used for copyright protection. However, most audio watermarking systems in the transform domain, such as 3 , 9 – 11 , 19 , are very time-consuming, especially for signals of long duration. These systems operate in a sequential manner (Fig.…”

Section: Introductionmentioning

confidence: 99%

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An efficient watermarking algorithm for digital audio data in security applications

Yamni,

Daoui,

Karmouni

et al. 2023

Sci Rep

Self Cite

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Transform-domain audio watermarking systems are more robust than time-domain systems. However, the main weakness of these systems is their high computational cost, especially for long-duration audio signals. Therefore, they are not desirable for real-time security applications where speed is a critical factor. In this paper, we propose a fast watermarking system for audio signals operating in the hybrid transform domain formed by the fractional Charlier transform (FrCT) and the dual-tree complex wavelet transform (DTCWT). The central idea of the proposed algorithm is to parallelize the intensive and repetitive steps in the audio watermarking system and then implement them simultaneously on the available physical cores on an embedded systems cluster. In order to have a low power consumption and a low-cost cluster with a large number of physical cores, four Raspberry Pis 4B are used where the communication between them is ensured using the Message Passing Interface (MPI). The adopted Raspberry Pi cluster is also characterized by its portability and mobility, which are required in watermarking-based smart city applications. In addition to its resistance to any possible manipulation (intentional or unintentional), high payload capacity, and high imperceptibility, the proposed parallel system presents a temporal improvement of about 70%, 80%, and 90% using 4, 8, and 16 physical cores of the adopted cluster, respectively.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An efficient watermarking algorithm for digital audio data in security applications

Yamni,

Daoui,

Karmouni

et al. 2023

Sci Rep

Self Cite

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show abstract

“…The signal after wavelet transformation [27,28] is distributed in m resolutions , and qj is the relative energy of each resolution. Remove the part with small entropy value, that is, the part with less redundancy and information, and keep the part with large entropy value, that is, the part with large uncertainty and more information.…”

Section: Wavelet Entropymentioning

confidence: 99%

COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine

Han

Wang

2022

EAI Endorsed Trans e-Learn

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In recent years, COVID-19 has spread rapidly among humans. Chest CT is an effective means of diagnosing COVID-19. However, the diagnosis of CT images still depends on the doctor's visual judgment and medical experience. This takes a certain amount of time and may lead to misjudgment. In this paper, a new algorithm for automatic diagnosis of COVID-19 based on chest CT image data was proposed. The algorithm comprehensively uses WE to extract image features, uses ELM for training, and finally passes k-fold CV validation. After evaluating and detecting performance on 296 chest CT images, our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, F1, MCC and FMI.

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“…This adds computational complexity and introduces approximation errors [11]. To this end, many researchers have started to use discrete orthogonal moments [12,13]. Zhu et al [14,15] demonstrated that discrete orthogonal moments are more effective than continuous orthogonal moments at representing images.…”

mentioning

confidence: 99%

An efficient computation of discrete orthogonal moments for bio-signals reconstruction

Fathi

Ahmed

Makhlouf

2022

EURASIP J. Adv. Signal Process.

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Bio-signals are extensively used in diagnosing many diseases in wearable devices. In signal processing, signal reconstruction is one of the essential applications. Discrete orthogonal moments (DOMs) are effective analysis tools for signals that can extract digital information without redundancy. The propagation of numerical errors is a significant challenge for the computation of DOMs at high orders. This problem damages the orthogonality property of these moments, which restricts the ability to recover the signal's distinct and unique components with no redundant information. This paper proposes a stable computation of DOMs based on QR decomposition methods: the Gram–Schmidt, Householder, and Given Rotations methods. It also presents a comparative study on the performance of the types of moments: Tchebichef, Krawtchouk, Charlier, Hahn, and Meixner moments. The proposed algorithm's evaluation is done using the MIT-BIH arrhythmia dataset in terms of mean square error and peak signal to noise ratio. The results demonstrate the superiority of the proposed method in computing DOMs, especially at high moment orders. Moreover, the results indicate that the Householder method outperforms Gram–Schmidt and Given Rotations methods in execution time and reconstruction quality. The comparative results show that Tchebichef, Krawtchouk, and Charlier moments have superior reconstruction quality than Hahn and Meixner moments, and Tchebichef generally has the highest performance in signal reconstruction.

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Robust audio watermarking scheme based on fractional Charlier moment transform and dual tree complex wavelet transform

Cited by 22 publications

References 32 publications

An efficient watermarking algorithm for digital audio data in security applications

An efficient watermarking algorithm for digital audio data in security applications

COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine

An efficient computation of discrete orthogonal moments for bio-signals reconstruction

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