  Recent advances in AI technology have made the forgery of digital images and
videos easier, and it has become significantly more difficult to identify such
forgeries. These forgeries, if disseminated with malicious intent, can
negatively impact social and political stability, and pose significant ethical
and legal challenges as well. Deepfake is a variant of auto-encoders that use
deep learning techniques to identify and exchange images of a person's face in
a picture or film. Deepfake can result in an erosion of public trust in digital
images and videos, which has far-reaching effects on political and social
stability. This study therefore proposes a solution for facial forgery
detection to determine if a picture or film has ever been processed by
Deepfake. The proposed solution reaches detection efficiency by using the
recently proposed separable convolutional neural network (CNN) and image
segmentation. In addition, this study also examined how different image
segmentation methods affect detection results. Finally, the ensemble model is
used to improve detection capabilities. Experiment results demonstrated the
excellent performance of the proposed solution.
