Data Science and Intelligent Computing Techniques
Combining Computer Vision Techniques and Intraframe Noise Methods to Detect a Deepfake
Authors: Maya P Shelke, Nihar Ranjan, Ajinkya Kharade, Pranav Gaikwad, Shubham Arakh and Analp Kalore
Publishing Date: 22-10-2023
ISBN: 978-81-955020-2-8
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.
Keywords
Deep Fake, intraframe noise, extracting features, high accuracy
Cite as
Maya P Shelke, Nihar Ranjan, Ajinkya Kharade, Pranav Gaikwad, Shubham Arakh and Analp Kalore, "Combining Computer Vision Techniques and Intraframe Noise Methods to Detect a Deepfake", In: Satyasai Jagannath Nanda and Rajendra Prasad Yadav (eds), Data Science and Intelligent Computing Techniques, SCRS, India, 2023, pp. 471-480. https://doi.org/10.56155/978-81-955020-2-8-43