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A Novel Method of Deepfake Detection
The findings show that the detection are generally domain-specific tasks, however that using Transfer Learning considerably improves the model performance parameters, whereas convolutional RNN gives sequence detection advantage.
Abstract
Deep-Fake is a novel artificial media technology that uses the likeness of someone else to replace people in existing photographs and films. Deep Learning, as the name implies, is a type of Artificial Intelligence that is used to create it. It is critical to develop counter attacking approaches for detecting fraudulent data. This research examines the Deep-Fake technology in depth. The Deep-Fake Detection discussed here is based on current datasets, such as the Deep-Fake Detection Challenge (DFDC) and Google’s Deep-Fake Detection dataset (DFD). The creation of a bespoke dataset from high-quality Deep-Fakes was utilised to test models. The results of both with and without Transfer Learning were analysed. Finally, the trained models were used to spot well-known deep-fakes of former US President Barack Obama and well-known actor Tom Cruise. A comparison study was performed on all three models. The findings show that the detection are generally domain-specific tasks, however that using Transfer Learning considerably improves the model performance parameters, whereas convolutional RNN gives sequence detection advantage.