Detecting Deepfake Videos: An Analysis of Three Techniques

• URL: http://arxiv.org/abs/2007.08517v1
• Date: Wed, 15 Jul 2020 20:36:23 GMT
• Title: Detecting Deepfake Videos: An Analysis of Three Techniques
• Authors: Armaan Pishori, Brittany Rollins, Nicolas van Houten, Nisha Chatwani, Omar Uraimov
• Abstract summary: Recent advances in deepfake generating algorithms 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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