Explainable Deepfake Video Detection using Convolutional Neural Network and CapsuleNet
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917645193838592 |
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| author | Ishrak, Gazi Hasin Mahmud, Zalish Farabe, MD. Zami Al Zunaed Tinni, Tahera Khanom Reza, Tanzim Parvez, Mohammad Zavid |
| author_facet | Ishrak, Gazi Hasin Mahmud, Zalish Farabe, MD. Zami Al Zunaed Tinni, Tahera Khanom Reza, Tanzim Parvez, Mohammad Zavid |
| contents | Deepfake technology, derived from deep learning, seamlessly inserts individuals into digital media, irrespective of their actual participation. Its foundation lies in machine learning and Artificial Intelligence (AI). Initially, deepfakes served research, industry, and entertainment. While the concept has existed for decades, recent advancements render deepfakes nearly indistinguishable from reality. Accessibility has soared, empowering even novices to create convincing deepfakes. However, this accessibility raises security concerns.The primary deepfake creation algorithm, GAN (Generative Adversarial Network), employs machine learning to craft realistic images or videos. Our objective is to utilize CNN (Convolutional Neural Network) and CapsuleNet with LSTM to differentiate between deepfake-generated frames and originals. Furthermore, we aim to elucidate our model's decision-making process through Explainable AI, fostering transparent human-AI relationships and offering practical examples for real-life scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12841 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Explainable Deepfake Video Detection using Convolutional Neural Network and CapsuleNet Ishrak, Gazi Hasin Mahmud, Zalish Farabe, MD. Zami Al Zunaed Tinni, Tahera Khanom Reza, Tanzim Parvez, Mohammad Zavid Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Deepfake technology, derived from deep learning, seamlessly inserts individuals into digital media, irrespective of their actual participation. Its foundation lies in machine learning and Artificial Intelligence (AI). Initially, deepfakes served research, industry, and entertainment. While the concept has existed for decades, recent advancements render deepfakes nearly indistinguishable from reality. Accessibility has soared, empowering even novices to create convincing deepfakes. However, this accessibility raises security concerns.The primary deepfake creation algorithm, GAN (Generative Adversarial Network), employs machine learning to craft realistic images or videos. Our objective is to utilize CNN (Convolutional Neural Network) and CapsuleNet with LSTM to differentiate between deepfake-generated frames and originals. Furthermore, we aim to elucidate our model's decision-making process through Explainable AI, fostering transparent human-AI relationships and offering practical examples for real-life scenarios. |
| title | Explainable Deepfake Video Detection using Convolutional Neural Network and CapsuleNet |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2404.12841 |