  The accelerated growth in synthetic visual media generation and manipulation
has now reached the point of raising significant concerns and posing enormous
intimidations towards society. There is an imperative need for automatic
detection networks towards false digital content and avoid the spread of
dangerous artificial information to contend with this threat. In this paper, we
utilize and compare two kinds of handcrafted features(SIFT and HoG) and two
kinds of deep features(Xception and CNN+RNN) for the deepfake detection task.
We also check the performance of these features when there are mismatches
between training sets and test sets. Evaluation is performed on the famous
FaceForensics++ dataset, which contains four sub-datasets, Deepfakes,
Face2Face, FaceSwap and NeuralTextures. The best results are from Xception,
where the accuracy could surpass over 99\% when the training and test set are
both from the same sub-dataset. In comparison, the results drop dramatically
when the training set mismatches the test set. This phenomenon reveals the
challenge of creating a universal deepfake detection system.
