Hybrid Deepfake Detection Utilizing MLP and LSTM

• URL: http://arxiv.org/abs/2304.14504v1
• Date: Fri, 21 Apr 2023 16:38:26 GMT
• Title: Hybrid Deepfake Detection Utilizing MLP and LSTM
• Authors: Jacob Mallet, Natalie Krueger, Mounika Vanamala, Rushit Dave
• Abstract summary: A deepfake is an invention that has come with the latest technological advancements. In this paper, we propose a new deepfake detection schema utilizing two deep learning algorithms. We evaluate our model using a dataset named 140k Real and Fake Faces to detect images altered by a deepfake with accuracies achieved as high as 74.7%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing reliance of society on social media for authentic information has done nothing but increase over the past years. This has only raised the potential consequences of the spread of misinformation. One of the growing methods in popularity is to deceive users using a deepfake. A deepfake is an invention that has come with the latest technological advancements, which enables nefarious online users to replace their face with a computer generated, synthetic face of numerous powerful members of society. Deepfake images and videos now provide the means to mimic important political and cultural figures to spread massive amounts of false information. Models that can detect these deepfakes to prevent the spread of misinformation are now of tremendous necessity. In this paper, we propose a new deepfake detection schema utilizing two deep learning algorithms: long short term memory and multilayer perceptron. We evaluate our model using a publicly available dataset named 140k Real and Fake Faces to detect images altered by a deepfake with accuracies achieved as high as 74.7%

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