By
Divya K.K,
Muhammad Rafnas K.M.,
Muhammed Ismail P,
Muhammed Minshad C,
- Professor Department of Computer Science and Engineering, P A College of Engineering, Mangalore Karnataka India
- Student Department of Computer Science and Engineering, P A College of Engineering, Mangalore Karnataka India
- Student Department of Computer Science and Engineering, P A College of Engineering, Mangalore Karnataka India
- Student Department of Computer Science and Engineering, P A College of Engineering, Mangalore Karnataka India
Abstract
This 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.
Keywords: Deepfake, ResNext, machine learning, deep learning, LSTM
How to cite this article: Divya K.K, Muhammad Rafnas K.M., Muhammed Ismail P, Muhammed Minshad C. Robustness of Deepfake Detection Systems Against Adversarial Attacks. Journal of Instrumentation Technology & Innovations. 2024; ():-.
How to cite this URL: Divya K.K, Muhammad Rafnas K.M., Muhammed Ismail P, Muhammed Minshad C. Robustness of Deepfake Detection Systems Against Adversarial Attacks. Journal of Instrumentation Technology & Innovations. 2024; ():-. Available from: https://journals.stmjournals.com/joiti/article=2024/view=167969
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Ahead of Print Subscription Original Research
Journal of Instrumentation Technology & Innovations
ISSN: 2249–4731
| Volume | |
| Received | June 9, 2024 |
| Accepted | June 28, 2024 |
| Published | August 14, 2024 |