Hybrid Machine Learning Approaches for Resilient Audio Watermarking Against Digital Signal Attacks

This article presents a novel audio watermarking method that enhances the safety of online distribution of digital audio materials against unauthorized access and manipulation. The system employs a secret key towards randomized watermark insertion and watermark insertion is both secure and unpredictable. Fast Fourier Transform (FFT) is used to analyze the frequencies of the audio. The watermark is then inserted into the chosen frequency bins averting perceptual distortion without deteriorating audio quality. Moreover, powerful encryption of data protects data on metadata which can include changed timestamps, frequency shifts, etc. The two-layer scheme is developed in such a way that it is tougher and sturdier and the watermark is also not lost when the audio is subjected to several processing attacks. Signal-to-Noise Ratio (SNR) and Bit Error Rate (BER) are used to determine the system performance and it shows that it is resistant to compression and additional noise. As experimental evidence, the proposed method provides a reasonable compromise between invisibility and security, which is why it is a stable method of implementing digital rights management (DRM) and intellectual property protection.