DEEP FAKE AUDIO DETECTION USING DEEP LEARNING

Abstract

The rapid advancement of artificial intelligence and deep learning technologies has led to the emergence of deep fake audio, where synthetic voices are generated to mimic real individuals with high accuracy. While these technologies have useful applications in entertainment and accessibility, they also pose serious threats in areas such as fraud, misinformation, and identity theft. Detecting deep fake audio has become a critical challenge in cybersecurity and digital forensics. This project proposes a deep learning-based system for detecting synthetic or manipulated audio by analyzing patterns and characteristics that differentiate real human speech from artificially generated voices. The proposed system utilizes advanced deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract features from audio signals. These features include spectral properties, frequency variations, and temporal patterns that are often difficult to replicate perfectly in synthetic audio. The system processes audio inputs by converting them into spectrograms, which visually represent the frequency distribution over time. These spectrograms are then fed into deep learning models for classification as real or fake audio. The model is trained using large datasets containing both genuine and deep fake audio samples to improve accuracy and generalization. Techniques such as data augmentation and noise filtering are applied to enhance model robustness. The system is capable of detecting subtle inconsistencies in speech patterns, making it effective against advanced voice synthesis techniques. Overall, the proposed solution provides a reliable and efficient approach to identifying deep fake audio. It can be applied in areas such as digital forensics, media verification, and secure authentication systems. By leveraging deep learning, the system contributes to combating the misuse of synthetic media and enhancing trust in digital communications.