Using Deep Learning to Detecting Deepfakes

• URL: http://arxiv.org/abs/2207.13644v1
• Date: Wed, 27 Jul 2022 17:05:16 GMT
• Title: Using Deep Learning to Detecting Deepfakes
• Authors: Jacob Mallet, Rushit Dave, Naeem Seliya, Mounika Vanamala
• Abstract summary: Deepfakes are videos or images that replace one persons face with another computer-generated face, often a more recognizable person in society. To combat this online threat, researchers have developed models that are designed to detect deepfakes. This study looks at various deepfake detection models that use deep learning algorithms to combat this looming threat.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the recent years, social media has grown to become a major source of information for many online users. This has given rise to the spread of misinformation through deepfakes. Deepfakes are videos or images that replace one persons face with another computer-generated face, often a more recognizable person in society. With the recent advances in technology, a person with little technological experience can generate these videos. This enables them to mimic a power figure in society, such as a president or celebrity, creating the potential danger of spreading misinformation and other nefarious uses of deepfakes. To combat this online threat, researchers have developed models that are designed to detect deepfakes. This study looks at various deepfake detection models that use deep learning algorithms to combat this looming threat. This survey focuses on providing a comprehensive overview of the current state of deepfake detection models and the unique approaches many researchers take to solving this problem. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper.

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