  The creation of altered and manipulated faces has become more common due to
the improvement of DeepFake generation methods. Simultaneously, we have seen
detection models' development for differentiating between a manipulated and
original face from image or video content. We have observed that most publicly
available DeepFake detection datasets have limited variations, where a single
face is used in many videos, resulting in an oversampled training dataset. Due
to this, deep neural networks tend to overfit to the facial features instead of
learning to detect manipulation features of DeepFake content. As a result, most
detection architectures perform poorly when tested on unseen data. In this
paper, we provide a quantitative analysis to investigate this problem and
present a solution to prevent model overfitting due to the high volume of
samples generated from a small number of actors. We introduce Face-Cutout, a
data augmentation method for training Convolutional Neural Networks (CNN), to
improve DeepFake detection. In this method, training images with various
occlusions are dynamically generated using face landmark information
irrespective of orientation. Unlike other general-purpose augmentation methods,
it focuses on the facial information that is crucial for DeepFake detection.
Our method achieves a reduction in LogLoss of 15.2% to 35.3% on different
datasets, compared to other occlusion-based augmentation techniques. We show
that Face-Cutout can be easily integrated with any CNN-based recognition model
and improve detection performance.
