Deep-Detector: Deepfake Voice Recognition using Machine Learning

Advancements in machine learning (ML) and artificial intelligence (AI) have paved the way for the deepfake spectrum to expand in the tech space. The growing demand for generative AI has enabled users to apply the technology in the fields of deepfake voice generation. Significant progress has been made in deepfake voice synthesis, which presents hurdles for conventional voice recognition systems. This paper offers a detailed analysis of the current state, approaches, and ramifications of deepfake voice recognition. A Convolution Neural Network (CNN) model is trained using a large dataset to identify features, which helps to classify data into distinct categories based on these characteristics. This study aims to observe the spectral patterns of an AI-generated sound, differentiate it from natural human speech, and classify them according to these features. The findings of this research contribute to the ongoing efforts to develop robust, deepfake voice recognition systems. By leveraging the potential of CNNs, the study seeks to enhance the accuracy and reliability of voice-based authentication mechanisms against emerging threats from synthetic voices. The outcomes are crucial for advancing the field and ensuring the secure deployment of voice recognition technologies in various applications. Additionally, we employed a machine learning pipeline to provide a structured and efficient workflow, facilitating the seamless detection of deepfake voices.