Deep learning approaches for robust deep fake detection
Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(03), 2283–2289
Article DOI: 10.30574/wjarr.2024.21.3.0889
Publication history:
Received on 12 February 2024; revised on 23 March 2024; accepted on 25 March 2024
Abstract:
Detecting deepfake images using a deep learning approach, particularly using model Densenet121, involves training a neural network to differentiate between authentic and manipulated images. Deepfakes have gained prominence due to advances in deep learning, especially generative adversarial networks (GANs). They pose significant challenges to the veracity of digital content, as they can be used to create realistic and deceptive media. Deepfakes are realistic looking fake media generated by many artificial intelligence tools like face2face and deepfake, which pose a severe threat to public. As more deepfakes are spreading, we really need better ways to find and prevent them. Deepfake involves creation of highly realistic images and videos and misuse them for spreading fake news, defaming individuals, and possess a significant threat to the integrity of digital content. Our project “Deep Learning Approaches for Robust Deep Fake Detection” aims to address this critical issue by developing a robust system for identification and localization of deep fake content by using ‘Densenet121’ model. This proposed framework seamlessly integrates forgery detection and localization. The dataset used in this project is “140k Real and Fake Faces”, and it consists of 70k real faces from Flickr dataset collected by Nvidia and 70k fake faces sampled from the 1 million Fake faces generated by StyleGAN. For localization purpose, we use GRAD-CAM method to accurately identify the morphed regions. Overall, our goal is to make deepfake detection more effective and reliable in today’s digital landscape.
Keywords:
Deepfakes; Densenet121; Generative Adversarial Networks; Localization; Gradcam
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0