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The ubiquitous use of digital media across various platforms has heightened the risk of copyright infringement and unauthorized distribution. Digital content such as images, audio, and video can be easily subjected to copyright violations if it is not adequately secured and protected using effective technological measures. In this paper, we explore different methods employed for safeguarding the copyright of digital media and propose a novel approach for copyright protection of audio files through the integration of watermarking techniques and neural networks. The proposed work concentrates on digital audio files. Our methodology leverages watermarking to embed ownership information or identifiers into audio files, ensuring their traceability and authenticity. Furthermore, we utilize neural networks, specifically encoder-decoder architecture, to enhance the robustness and security of the audio watermarking system. The primary objective of this innovative approach is to ensure robust protection of digital media without degrading the audio quality or clarity of embedded images. Utilizing sophisticated signal processing techniques, including wavelet transforms and denoising algorithms, the system embeds and subsequently reconstructs watermarked images within audio files with high fidelity. The goal is to strike an optimal balance between security and usability, providing content creators with a reliable method to safeguard their intellectual property. We evaluate the proposed method's performance against critical parameters such as Maximum Correlation and Peak Signal-to-Noise Ratio (PSNR), among others. By training neural networks to embed watermarks imperceptibly and detect them accurately, we aim to provide a robust solution for copyright protection in the digital audio domain.
Patil et al. (Wed,) studied this question.