  Recent advances in deepfake generating algorithms that produce manipulated
media have had dangerous implications in privacy, security and mass
communication. Efforts to combat this issue have risen in the form of
competitions and funding for research to detect deepfakes. This paper presents
three techniques and algorithms: convolutional LSTM, eye blink detection and
grayscale histograms-pursued while participating in the Deepfake Detection
Challenge. We assessed the current knowledge about deepfake videos, a more
severe version of manipulated media, and previous methods used, and found
relevance in the grayscale histogram technique over others. We discussed the
implications of each method developed and provided further steps to improve the
given findings.
