High-capacity, transparent and robust audio watermarking based on synergy between DCT transform and LU decomposition using genetic algorithm

Mosleh, Mahdi; Setayeshi, Saeed; Barekatain, Behrang; Mosleh, Mohammad

doi:10.1007/s10470-019-01464-4

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Signal to Noise Ratio (SNR) is a statistical difference metric which is used to measure the similitude between the undistorted original audio signal and the distorted watermarked audio signal. Therefore define cost function as following: (11) Where x (n) corresponds to the original signal, and x'(n) corresponds to the watermarked signal.…”

Section: Watermark Embedding Procedurementioning

confidence: 99%

“…The robustness versus MP3 agglomeration of their algorithm is apperceived to be extremely sufficient; although, this algorithm was not appraised vis-à-vis further attacks. Mosleh and et all suggested a Multi-objective blind audio watermarking schemes based genetic algorithm where is simultaneously improves the capacity, transparency and persistence factors [7]. Mosleh showing that with optimization of frame length and frequency sub-band the payload capacity with 2002 bit per second can be received where is the transparency and robustness have good qualify.…”

Section: Introductionmentioning

confidence: 99%

“…The primary allotment of this survey is a secure and high-capacity audio watermarking mechanism that represents sufficient robustness against challenging signal processing attacks. The strategic selection of this paper is that two step optimization algorithm used to determine the optimum sub-band in the embedding process and the extraction coefficients in the extraction process in which the highest feasible payload capacity whit the good audio SNR and extracted image BER achieved.Evolutionary algorithms are a set of optimization algorithms in which do not require sophisticated mathematical operations such as derivative and integral to obtain the best response [9][10][11]. One of the interest evolutionaryalgorithms is TLBO algorithm.…”

Section: Introductionmentioning

confidence: 99%

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High-capacity, transparent and robust blind audio watermarking scheme based DWT and TLBO algorithm

Joudaki

Abdeyazdan

Mosleh

2020

Preprint

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Digital watermarking is one of the best solutions again the copyright infringement, duplicates, verifies data and illegal distribution of digital media. Recently, the protection of digital audio signals is one of the attracting and interesting topics for scientific and researchers. In this paper we propose a blind audio watermarking mechanism in which it has high capacity, transparency and resistance simultaneously based on digital wavelet transform (DWT) algorithm. The key principle of this work is that in the DWT procedure, using two filters; break down the original audio signal into several sub-bands and transform them on a specific frequency range. It should be noted that the 8 bits of watermarked signal is selected and transform to the original signal. In order to increase the watermarking resistance, framing the high frequency coefficients of the third level of the wavelet and calculate the frames average and place them in the frame memory prime. Moreover, TLBO algorithm used to determination of embedding and extraction coefficients in order to increase the SNR ratio in the embedding process and decrease the bit error rate (BER) in the extraction process. This method increases the embedding payload capacity while the audio SNR and extracted image BER have good qualify. Moreover, experimental results shown that this method has 13kbs hiding rate,ascendency imperceptibility, good payload capacity and intense robustness when resisting against various attacks such as MP3 compression, re-quantization, low-pass filtering, amplitude scaling, re-sampling, echo addition and noise corruption.

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Section: Watermark Embedding Procedurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-capacity, transparent and robust blind audio watermarking scheme based DWT and TLBO algorithm

Joudaki

Abdeyazdan

Mosleh

2020

Preprint

Self Cite

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“…Finally, based on Fibonacci sequence numbers, the watermark is embedded into upper-triangular matrices. The most important weakness and limitation of this system is its low robustness when achieving high capacities, due to the use of a nonintelligent extraction method [13]. Pourhashemi et al have embedded watermark using fast Fourier transform (FFT) as well as Lucas regular sequence and a recursive process for optimal parameter tuning.…”

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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A novel quantum steganography-Steganalysis system for audio signals

Chaharlang

Mosleh

Heikalabad

2020

Multimed Tools Appl

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High-capacity, transparent and robust audio watermarking based on synergy between DCT transform and LU decomposition using genetic algorithm

Cited by 5 publications

References 31 publications

High-capacity, transparent and robust blind audio watermarking scheme based DWT and TLBO algorithm

High-capacity, transparent and robust blind audio watermarking scheme based DWT and TLBO algorithm

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

A novel quantum steganography-Steganalysis system for audio signals

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