Abstract
Deepfake algorithms can make forged pictures and videos that people cannot differentiate them from true ones. The suggestion of technology that locates and proves the truth of virtual visual media is as a result essential. Deepfakes generate realistic forged images or videos of targeted persons by swapping their faces another person saying or doing things that are not really done by them and public start trusting in such forged videos, as it is not identifiable with the normal human eye. This paper offers a novel method to locate Deepfakes. We present discussions on challenges, studies, advances and strategies associated with Deepfake along with the method based on long-term recurrent convolution network (LRCN). By reviewing the history of Deepfakes and cutting-edge Deepfake detection strategies, this method will give a new approach towards the Deepfake technology and will help in the advancement of new and strong strategy to deal with an increasing number of Deepfakes.
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Deshmukh, A., Wankhade, S. (2021). Deepfake Detection by Exposing AI-Generated Fake Face Video. In: Singh Mer, K.K., Semwal, V.B., Bijalwan, V., Crespo, R.G. (eds) Proceedings of Integrated Intelligence Enable Networks and Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6307-6_69
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DOI: https://doi.org/10.1007/978-981-33-6307-6_69
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