Abstract
As humanity cruises ahead in its artificial intelligence race to reach new horizons in the field, especially in the image processing arena, there have also emerged some “flies” in the ointment along the way. One such fly is the recent application known as deepfake. Deepfake signifies the media which is morphed using deep learning and artificial intelligence tools. These types of media cannot be distinguished easily and can fool one on the first instance. To counter deepfakes, deep learning is used in this paper. Here, convolutional neural networks (CNNs) are used to classify images from our dataset used (deepfake dataset). Deep learning networks to learn features of the deepfake images and the predict if an image is real or deepfake. CNNs are used as these have proved to be robust in the deep learning applications which deal with images.
The author is also an Assistant Software Engineer, at Centre for Railway Information Systems (CRIS), an organization under Ministry of Railways, Government of India.
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Acknowledgements
The research done in this paper is a part of M.Tech thesis work of the author, and it was done in the supervision of Prof. Manoj Madhava Gore, without whose advice and constructive criticism, it could have not been completed.
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Pal Singh, P. (2023). DFCNNet: A Convolutional Neural Network to Detect Deepfakes. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Infrastructure and Computing. Lecture Notes in Networks and Systems, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-19-5331-6_8
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DOI: https://doi.org/10.1007/978-981-19-5331-6_8
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