  Deepfakes are synthetically generated images, videos or audios, which
fraudsters use to manipulate legitimate information. Current deepfake detection
systems struggle against unseen data. To address this, we employ three
different deep Convolutional Neural Network (CNN) models, (1) VGG16, (2)
InceptionV3, and (3) XceptionNet to classify fake and real images extracted
from videos. We also constructed a fusion of the deep CNN models to improve the
robustness and generalisation capability. The proposed technique outperforms
state-of-the-art models with 96.5% accuracy, when tested on publicly available
DeepFake Detection Challenge (DFDC) test data, comprising of 400 videos. The
fusion model achieves 99% accuracy on lower quality DeepFake-TIMIT dataset
videos and 91.88% on higher quality DeepFake-TIMIT videos. In addition to this,
we prove that prediction fusion is more robust against adversarial attacks. If
one model is compromised by an adversarial attack, the prediction fusion does
not let it affect the overall classification.
