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Deepfake Detection Using ResneXt Deep Learning Architecture
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AI
DOI：10.1007/978-981-96-7496-1_17.png)
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Abstract
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A deepfake is a misleading video or image that looks genuine. Generative Adversarial Networks (GANs) are the known name in the domain of machine learning. GANs generate a huge amount of fake human writing with deep-learning-wide-models. The generator model learns to sample points from a latent space so that new samples of the same distribution can be fed in and produce different observable model outputs. Deepfakes for most applications can be convincingly created using Generative Adversarial Networks (GANs). There are fears on the Internet related to deepfake. However, the authors use ResneXt and LSTMs for using Deep Learning Network to identify fake areas of deepfake uses Python facial recognition and C++ visual libraries to identify a face in this video. Fake videos are further validated using models trained on various edge groups.
Keywords:
Deepfake
Machine learning
CNN
Generative adversarial networks (GANs)
Anomaly detection
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