  Significant advances in deep learning have obtained hallmark accuracy rates
for various computer vision applications. However, advances in deep generative
models have also led to the generation of very realistic fake content, also
known as deepfakes, causing a threat to privacy, democracy, and national
security. Most of the current deepfake detection methods are deemed as a binary
classification problem in distinguishing authentic images or videos from fake
ones using two-class convolutional neural networks (CNNs). These methods are
based on detecting visual artifacts, temporal or color inconsistencies produced
by deep generative models. However, these methods require a large amount of
real and fake data for model training and their performance drops significantly
in cross dataset evaluation with samples generated using advanced deepfake
generation techniques. In this paper, we thoroughly evaluate the efficacy of
deep face recognition in identifying deepfakes, using different loss functions
and deepfake generation techniques. Experimental investigations on challenging
Celeb-DF and FaceForensics++ deepfake datasets suggest the efficacy of deep
face recognition in identifying deepfakes over two-class CNNs and the ocular
modality. Reported results suggest a maximum Area Under Curve (AUC) of 0.98 and
an Equal Error Rate (EER) of 7.1% in detecting deepfakes using face recognition
on the Celeb-DF dataset. This EER is lower by 16.6% compared to the EER
obtained for the two-class CNN and the ocular modality on the Celeb-DF dataset.
Further on the FaceForensics++ dataset, an AUC of 0.99 and EER of 2.04% were
obtained. The use of biometric facial recognition technology has the advantage
of bypassing the need for a large amount of fake data for model training and
obtaining better generalizability to evolving deepfake creation techniques.
