An effective digital audio watermarking using a deep convolutional neural network with a search location optimization algorithm for improvement in Robustness and Imperceptibility

Watermarking is the advanced technology utilized to secure digital data by integrating ownership or copyright protection. Most of the traditional extracting processes in audio watermarking have some restrictions due to low reliability to various attacks. Hence, a deep learning-based audio watermarki...

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Vydáno v:High-Confidence Computing Ročník 3; číslo 4; s. 100153
Hlavní autoři: Patil, Abhijit J., Shelke, Ramesh
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.12.2023
Elsevier
Témata:
Deep convolutional neural network
DWT
Imperceptibility
Robustness
Search location optimization algorithm
Search location optimization algorithm
DWT
Imperceptibility
Robustness
Deep convolutional neural network
ISSN:2667-2952, 2667-2952
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Shrnutí:Watermarking is the advanced technology utilized to secure digital data by integrating ownership or copyright protection. Most of the traditional extracting processes in audio watermarking have some restrictions due to low reliability to various attacks. Hence, a deep learning-based audio watermarking system is proposed in this research to overcome the restriction in the traditional methods. The implication of the research relies on enhancing the performance of the watermarking system using the Discrete Wavelet Transform (DWT) and the optimized deep learning technique. The selection of optimal embedding location is the research contribution that is carried out by the deep convolutional neural network (DCNN). The hyperparameter tuning is performed by the so-called search location optimization, which minimizes the errors in the classifier. The experimental result reveals that the proposed digital audio watermarking system provides better robustness and performance in terms of Bit Error Rate (BER), Mean Square Error (MSE), and Signal-to-noise ratio. The BER, MSE, and SNR of the proposed audio watermarking model without the noise are 0.082, 0.099, and 45.363 respectively, which is found to be better performance than the existing watermarking models.
ISSN:2667-2952
2667-2952
DOI:10.1016/j.hcc.2023.100153