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Thursday, October 28 • 9:00pm - Friday, December 3 • 6:00pm
Audio Watermarking Technique Integrating Spread Spectrum and CNN-autoencoder

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This e-Brief proposes a novel approach of audio watermarking based on Spread Spectrum (SS) involves the psychoacoustic model and deep learning Convolutional Neural Networks (CNN)-autoencoder. Moreover, logistic chaotic maps are employed to enhance the security level of the method. First, a compressed image produced from the CNN-autoencoder is fed to the image encryption stage to yield an encrypted image to be used as the watermark. To apply image encryption, the plain image is, at first 8-bit binary-coded and shuffled by M-sequence. Next, each encoded image is diffused with a different chaotic sequence. Within the embedding phase, the psychoacoustic model is employed to shape the amplitude of the watermark signal which guarantees high inaudibility, whereas a logistic chaotic map is used to determine the positions for watermark embedding in a random manner. This scheme offers an extremely efficient and practical method as it can be used by institutions and companies for embedding their logos or trademarks as a watermark in audio products as the scheme utilizes RGB images. Experimental results show that the transparency and imperceptibility of the proposed algorithm are satisfactory also good image quality even against various attacks. The validity of the proposed audio watermarking method is demonstrated by simulation results.


Noha Korany

Electrical Engineering Department, Faculty of Engineering, Alexandria

Namat Elboghdadly

Electrical Engineering Department, Faculty of Engineering, Alexandria

Mohamed Elabdein

Instructor, Electrical Engineering Department, Alexandria Higher Institute of Engineering and Technology, Alexandria
Audio watermarkingAcoustics

Thursday October 28, 2021 9:00pm - Friday December 3, 2021 6:00pm EST