Deepfake anomaly detection using kernel density estimation based convolutional autoencoder

Abstract

The rise of deepfake technology has fuelled the need for robust detection methods to counter the increasing sophistication of manipulated multimedia content. Research has shown that humans struggle to identify deepfakes, highlighting the pressing need for AI-based solutions to safeguard the integrity of the information we consume daily. This paper proposes a novel approach CAE-DAD (Convolutional Autoencoder Deepfake Anomaly Detector) which facilitates deepfake detection through visual anomaly identification using convolutional autoencoders (CAEs). Methods like Kernel Density Estimation are used in the latent space to widen the anomaly score gap between real and fake images. The model is trained only on real face images. Existing models like VGG16 which are known for their feature extraction capabilities as incorporated as part of our encoder. The model is benchmarked against state-of-the-art methods and establish a comprehensive set of evaluation metrics, including accuracy, precision, recall, F1 score. The model achieves comparable performance to existing two-class training methods, and achieves 91% accuracy. The proposed anomaly identification using CAEs serves as a valuable addition to the arsenal of tools aimed at mitigating the risks associated with the proliferation of deepfake content.