DEEP FAKE AUDIO DETECTION USING DEEP LEARNING TECHNIQUES

Abstract

The rapid advancement of deepfake technology has created serious concerns regarding the authenticity and reliability of multimedia content, particularly audio recordings. Manipulated audio can be used for misinformation, fraud, and other malicious activities, making its detection a critical task. To address this issue, this project proposes a deep learningbased framework for identifying deepfake audio with high accuracy. The study begins with a detailed review of existing deepfake audio detection methods, highlighting their strengths and limitations, especially in handling complex audio manipulations. Based on this analysis, a novel deep learning architecture is developed to effectively identify subtle patterns and inconsistencies that distinguish genuine audio from manipulated samples. The proposed system incorporates specialized feature extraction techniques designed to capture the unique properties of audio signals. These features are then processed using deep neural network models trained on large-scale datasets containing both authentic and deepfake audio recordings. This approach enables the system to learn intricate patterns associated with different types of audio forgeries. Extensive experiments are conducted to evaluate the performance and robustness of the model across various manipulation techniques and levels of complexity. The results demonstrate the system’s capability to accurately detect deepfake audio, making it a reliable solution for enhancing media authenticity and security in modern digital environments