Deep fake Detection Through Deep Learning

Citations Over Time

Abstract

Abstract: Deep fake is a rapidly growing concern in society, and it has become a significant challenge to detect such manipulated media. Deep fake detection involves identifying whether a media file is authentic or generated using deep learning algorithms. In this project, we propose a deep learning-based approach for detecting deep fakes in videos. We use the Deep fake Detection Challenge dataset, which consists of real and Deep fake videos, to train and evaluate our deep learning model. We employ a Convolutional Neural Network (CNN) architecture for our implementation, which has shown great potential in previous studies. We pre-process the dataset using several techniques such as resizing, normalization, and data augmentation to enhance the quality of the input data. Our proposed model achieves high detection accuracy of 97.5% on the Deep fake Detection Challenge dataset, demonstrating the effectiveness of the proposed approach for deep fake detection. Our approach has the potential to be used in real-world scenarios to detect deep fakes, helping to mitigate the risks posed by deep fakes to individuals and society. The proposed methodology can also be extended to detect in other types of media, such as images and audio, providing a comprehensive solution for deep fake detection.

Related Papers

• → Small aircraft detection using deep learning(2021)19 cited
• → Implementing convolutional neural network model for prediction in medical imaging(2022)6 cited
• → Leaf Features Extraction for Plant Classification using CNN(2021)7 cited
• → Deep Convolution Neural Network for RBC Images(2022)2 cited
• → Deep Convolutional Neural Networks(2021)8 cited