  Deeplearning has been used to solve complex problems in various domains. As
it advances, it also creates applications which become a major threat to our
privacy, security and even to our Democracy. Such an application which is being
developed recently is the "Deepfake". Deepfake models can create fake images
and videos that humans cannot differentiate them from the genuine ones.
Therefore, the counter application to automatically detect and analyze the
digital visual media is necessary in today world. This paper details retraining
the image classification models to apprehend the features from each deepfake
video frames. After feeding different sets of deepfake clips of video fringes
through a pretrained layer of bottleneck in the neural network is made for
every video frame, already stated layer contains condense data for all images
and exposes artificial manipulations in Deepfake videos. When checking Deepfake
videos, this technique received more than 87 per cent accuracy. This technique
has been tested on the Face Forensics dataset and obtained good accuracy in
detection.
