Enhanced Deepfake Detection Leveraging Multi-Resolution Wavelet Convolutional Networks

Deep learning techniques have made it much easier to generate realistic fake content by superimposing or replacing existing images, videos, or audio with highly realistic alternative content. Due to the potential misuse of deepfakes for malicious purposes, the development of detection techniques and policies to mitigate harmful effects of deepfakes has become an important research area. In this paper, we combine both spatial and multi-resolution wavelet features to develop a simple yet effective model to detect deepfakes. We perform cross domain evaluations to compare the performance of the proposed model and state-ofthe-art peer models in terms of Area Under Curve (AUC) and Equal Error Rate (EER) metrics. Our extensive experimental results demonstrate that the proposed multi-resolution VGG19 model offers a more robust solution than other compared models to combating the proliferation of fake multimedia content on digital platforms.