Deepfake Video Detection using Neural Networks

Registration ID: IJNRD_220731

Published ID: IJNRD2405278

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Divyansh Sahu, Shivam Kabra, Rajat Gore, Apoorva Kharya, Dr. M K Jayanthi Kannan

Deepfake Detection, Neural Networks, Machine Learning, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN).

The proliferation of deepfake videos in recent years has raised significant concerns regarding the manipulation of digital media and its potential consequences on society. Detecting such videos has become a critical area of research to combat misinformation and preserve the integrity of visual content. This paper presents a comprehensive approach to deepfake video detection using neural networks. We propose a novel framework that leverages the power of deep learning techniques to accurately discern between authentic and manipulated videos. Our methodology involves preprocessing the video data, extracting relevant features, and training a deep neural network model for classification. Key features include facial landmarks, temporal patterns, and inconsistencies in pixel-level details, which are extracted using state-of-the-art computer vision techniques. Furthermore, we introduce a diverse dataset containing both real and synthetic videos, annotated with ground truth labels, to facilitate model training and evaluation. The dataset encompasses a wide range of scenarios and variations in deepfake generation techniques, ensuring robustness and generalization of the proposed detection system. To validate the effectiveness of our approach, extensive experiments are conducted on the dataset using various neural network architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The results demonstrate promising performance in accurately detecting deepfake videos across different contexts and manipulation levels. Overall, this research contributes to the ongoing efforts in combating the spread of misinformation and protecting the authenticity of visual media in the digital age. By leveraging advances in neural network technology, our approach offers a promising solution to the challenging problem of deepfake video detection, paving the way for more secure and trustworthy communication channels in the future.

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Divyansh Sahu, Shivam Kabra, Rajat Gore, Apoorva Kharya, & Dr. M K Jayanthi Kannan (May-2024). Deepfake Video Detection using Neural Networks . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), c765-c769. https://ijnrd.org/papers/IJNRD2405278.pdf

Volume 9 Issue 5, May-2024

Pages : c765-c769

Paper Reg. ID: IJNRD_220731

Published Paper Id: IJNRD2405278

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Naramdapuram, Madhya Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405278.pdf

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