  Deepfakes are videos that include changes, quite often substituting face of a
portrayed individual with a different face using neural networks. Even though
the technology gained its popularity as a carrier of jokes and parodies it
raises a serious threat to ones security - via biometric impersonation or
besmearing. In this paper we present two methods that allow detecting Deepfakes
for a user without significant computational power. In particular, we enhance
MesoNet by replacing the original activation functions allowing a nearly 1%
improvement as well as increasing the consistency of the results. Moreover, we
introduced and verified a new activation function - Pish that at the cost of
slight time overhead allows even higher consistency.
  Additionally, we present a preliminary results of Deepfake detection method
based on Local Feature Descriptors (LFD), that allows setting up the system
even faster and without resorting to GPU computation. Our method achieved Equal
Error Rate of 0.28, with both accuracy and recall exceeding 0.7.
