Abstract
Deep fakes are synthetic videos created by deep learning algorithms that can convincingly depict individuals saying or doing things they never did. With the proliferation of deepfake videos in social media and the potential for them to cause significant harm, deep fake detection has become a pressing issue. In this study, we offer a unique method for detecting deepfakes by combining computer vision methods with intraframe noise. The proposed approach involves extracting features from the video frames, including texture, color, and edges, and then adding a layer of intraframe noise to the video frames. We tested the proposed method on a variety of benchmark datasets and found that it achieved high accuracy in detecting deepfake videos.
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