Audio watermarking method using QR decomposition and genetic algorithm

Mohsenfar, Seyed Mohammadreza; Mosleh, Mohammad; Barati, Ali

doi:10.1007/s11042-013-1694-3

Cited by 24 publications

(10 citation statements)

References 23 publications

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“…The QR Decomposition of the quadratic matrix A is decomposition A, which is defined by Eq. ( 8) [21,22]: (8) where is an orthogonal matrix with . and are an upper triangular matrix.…”

Section: Qr Decompositionmentioning

confidence: 99%

High Payload Qr-Based Data Hiding Using Secured Compressed Watermark in Polar Domain

Irawati¹,

Budiman²,

Masykuroh³

et al. 2021

Frontiers in Artificial Intelligence and Applications

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Audio Watermarking is a method to insert a copyright marker on audio. This method inserts a watermark in the information form and in a way that does not damage the audio. This technique is one of the ways to solve the problem of copyright infringement. The embedded watermark has to meet the condition of not damaging the audio and must have robustness, imperceptibility, and good capacity. The data hiding technique use the combined method of Lifting Wavelet Transform (LWT), Fast Fourier Transform (FFT), QR Decomposition and Reconstruction, and Cartesian-Polar Transformation (CPT) based on Quantization Index Modulation (QIM) with the secured and compressed watermark using Compressive Sampling (CS) technique. The proposed scheme is blind Audio Watermarking as it no needs for original audio in the detection process. The combination of methods overcomes multiple attacks with guaranteed quality watermarking and high capacity. Compared to the existing technique, the data hiding technique can withstand LPF attacks, Resampling, Linear speed change (LSC), and MP3 compression. This proposed technique is also secured due to the coded watermark by a particular random key using CS. Combining CS and Audio Watermarking techniques can perform well in capacity, imperceptibility, security, and attack resistance.

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“…The QR Decomposition of the quadratic matrix A is decomposition A, which is defined by Eq. ( 8) [21,22]: (8) where is an orthogonal matrix with . and are an upper triangular matrix.…”

Section: Qr Decompositionmentioning

confidence: 99%

High Payload Qr-Based Data Hiding Using Secured Compressed Watermark in Polar Domain

Irawati¹,

Budiman²,

Masykuroh³

et al. 2021

Frontiers in Artificial Intelligence and Applications

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show abstract

“…Several methods proposed previously involved in inserting watermarks in the time domain [11–21] or frequency domain [3–10, 22–25]. In this study, we focus on the frequency domain especially the wavelet domain.…”

Section: Introductionmentioning

confidence: 99%

Digital audio signal watermarking using minimum‐energy scaling optimisation in the wavelet domain

Hsu

Tu²,

Yang³

et al. 2020

IET signal process.

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This work's contributions include three innovative concepts, an improved model, two‐stage Lagrange principle, and minimum‐energy scaling optimisation, for quantisation audio watermarking in the wavelet domain. First, discrete wavelet transform (DWT) multi‐coefficients quantisation, composed of arbitrary scaling on the lowest DWT coefficients, and the group‐based signal‐to‐noise ratio (SNR) of these coefficients is connected in a model. Then, the two‐stage Lagrange principle and minimum‐energy approach play two essential roles to obtain the optimal scaling factors. With the proposed scheme, the best fidelity and robustness of embedded audio can be attained and the perceptual evaluation of audio quality (PEAQ) test with an illustration of the relationship between SNR and PEAQ is also performed as well. Simulation results show that each watermarked audio by the proposed method attains a high SNR, good PEAQ, and a low bit error rate (BER). The SNR of most watermarked audios in their method is above 35 or even above 40 and the corresponding subjective difference grade of PEAQ is close to 0. In terms of comparing BER, most of their BER is as low as 2% or less indicating better robustness against many attacks, such as re‐sampling, amplitude scaling, and mp3 compression.

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“…The robustness of the system at high capacities has not been tested. Also, the detection method in this system is semiblind and has problems for implementation in industrial applications [19]. Chen et al have used low-frequency coefficients of DWT.…”

Section: Introductionmentioning

confidence: 99%

A novel audio watermarking scheme using ensemble-based watermark detector and discrete wavelet transform

Pourhashemi

Mosleh

Erfani

2020

Neural Comput & Applic

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Most existing extraction techniques in audio watermarking use conventional techniques in which some sets of special rules based on reverse embedding rules are used for watermark extraction and have many weaknesses, like very low robustness to destructive attacks. To overcome this problem, the use of machine learning-based methods has increased in recent years in this field. The disadvantage of these methods is the high reliance on a unique classifier and lack of proper efficiency when achieving high capacity, which is a major challenge in audio watermarking. The main purpose of this paper is to present a method that covers the weak points of conventional methods and simple intelligent methods and improves system performance using a synergistic combination of discrete wavelet transform (DWT) and ensemble-intelligent extraction approach by proposed combination of trained machine learning classifiers. For the embedding operation in the proposed method, the DWT and the difference in energy levels obtained through DWT coefficients are used. In the extraction section, three methods are used in parallel: (a) the trained support vector machine (SVM) classifier with RBF kernel, (b) trained SVM classifier with quadratic kernel and (c) the trained K-nearest neighbor classifier; finally, the majority function is used to vote and make a final decision to create an intelligent-based watermark detector. A training set is required to train the classifiers, whose bit sequence is generated by a proposed 5-bit linear-feedback shift register. The results of various experiments indicate that this ensemble method has achieved the appropriate imperceptibility and high capacity, along with higher robustness compared to conventional techniques and individual learning classifiers.

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Audio watermarking method using QR decomposition and genetic algorithm

Cited by 24 publications

References 23 publications

High Payload Qr-Based Data Hiding Using Secured Compressed Watermark in Polar Domain

High Payload Qr-Based Data Hiding Using Secured Compressed Watermark in Polar Domain

Digital audio signal watermarking using minimum‐energy scaling optimisation in the wavelet domain

A novel audio watermarking scheme using ensemble-based watermark detector and discrete wavelet transform

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