In the evolving technological landscape, the rise in the number of deepfake videos has risen by a marginally great amount. Deepfake videos are videos which are created using digital software, machine learning and face swapping. Deepfakes are artificially generated videos in which images are combined to create events and statements that never happened or never had been said. This brings our attention to the need for useful detection techniques that can tell whether videos are authentic or if they have been artificially generated using AI. Typically, a majority of deepfake videos are widespread using mobile applications such as WhatsApp, Facebook, Telegram and a variety of other mobile phone applications. This brings to light the problem of individuals not being able to differentiate between real videos and Deepfake videos which creates a need for detection software which people can directly use on their phones. The approach the author proposes, an multimodal based approach where the video modality consists of a distilled Vision Transformer model and the video modality consists of a modified CNN light-weight architecture. The research also looks at the feature extraction methods that are optimal for lightweight audio deepfake detection.
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