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Divya K.K, Muhammad Rafnas K.M., Muhammed Ismail P, Muhammed Minshad C,
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- Professor, Student, Student, Student Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Department of Computer Science and Engineering, P A College of Engineering, Mangalore Karnataka, Karnataka, Karnataka, Karnataka India, India, India, India
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Abstract
nThis paper explores a deep learning system to detect deepfake videos, a common type of fake media. With the use of sophisticated methods such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), our system can reliably discern between authentic and altered videos. It analyzes both the images and the audio in videos to find signs of deepfake manipulation. We process video frames and audio, extract features with CNNs and RNNs, and combine these features to decide if a video is real or fake. To ensure the dependability of our system, we trained and tested it on enormous datasets of both actual and fraudulent videos. Our project helps fight misinformation and protect the authenticity of digital content.
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Keywords: Deepfake, ResNext, machine learning, deep learning, LSTM
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Instrumentation Technology & Innovations(joiti)]
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References
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Journal of Instrumentation Technology & Innovations
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| Volume | ||
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | ||
| Received | June 9, 2024 | |
| Accepted | June 28, 2024 | |
| Published | August 14, 2024 |
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