Published Paper Details:

Artificial intelligence technologies have revolutionized the way we create and manipulate audio, video, images, and text. One of the most notable applications is deepfake content, which uses sophisticated techniques to generate convincing simulations of reality. However, researchers have been developing methods to detect and identify deepfake audio, thus enhancing security in various fields such as media forensics and authentication systems. One such method involves leveraging Mel Spectrograms and Convolutional Neural Networks (CNNs). Mel Spectrograms are visual representations of audio signals that display the frequency components over time. By analyzing these spectrograms, CNNs can be trained to identify patterns and anomalies that indicate artificial alterations in audio content. To develop an effective deepfake detection system, researchers utilized a dataset called Fake-or-Real, which contains a mix of real and deepfake audio samples. The dataset is classified into sub-datasets based on audio length and bit rate, providing a diverse range of samples for comprehensive model training. The trained CNN model can accurately distinguish between real and deepfake audio by identifying subtle or irregularities left behind by deepfake creators. These discrepancies serve as indicators of manipulation and help enhance audio security by automating the detection process. By integrating Mel Spectrograms and CNNs, this approach represents a significant advancement in combating deepfake technology. It offers a promising solution for organizations and individuals looking to protect against misinformation, fraudulent recordings, and other forms of audio manipulation. Moving forward, continued research and refinement of these techniques will further bolster trust and integrity in audio content across various domains, ensuring a safer and more secure digital environment.