Neural Audio Generation (GAN) Countermeasure Network Model Based on Deep Learning

Abstract

The proliferation of Generative Adversarial Networks (GANs) has ushered in a new era of audio synthesis, blurring the distinction between real and synthetic audio content. In response to the growing concerns surrounding the misuse of GAN-generated audio, this study presents a novel Neural Audio Generation Countermeasure Network Model based on deep learning techniques. The model is designed to detect and differentiate between real and GAN-generated audio with high accuracy and reliability. Leveraging a hybrid architecture combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the proposed model extracts spatial and temporal features from audio spectrograms to discern subtle patterns indicative of synthetic generation. Experimental evaluation demonstrates the model's effectiveness in mitigating the risks associated with synthetic audio manipulation, offering a promising solution for ensuring the integrity and authenticity of audio content in various applications. The study also discusses implications, limitations, and future directions for advancing the field of audio processing and security.