Enhancing Deepfake Detection with Audio Spectrograms and Siamese Networks

Abstract

Deepfake technology poses significant threats to digital security and media integrity, necessitating robust detection methods. This study introduces a novel approach for enhancing deepfake detection by leveraging unique techniques for analyzing audio data from multiple benchmark datasets. By extracting audio features from Mel, Delta Mel, and Delta-Delta Mel spectrograms, applying image processing techniques to these features, and employing Siamese networks, our method demonstrated unprecedented performances with FakeAVCeleb and MLAAD datasets. Through extensive experimentation, our approach achieved near-perfect accuracy in both validation and testing phases among these datasets. This significant improvement in accuracy, particularly through the focused analysis of audio characteristics, offers a promising direction for developing more robust and resilient deepfake detection systems. These findings highlight the potential of our technique as a promising solution for combating the malicious use of deepfake technology and provides a strong foundation for future research in multimedia forensics and cybersecurity.