Abstract
Deep learning is utilized as a deepfake detection and creation method. Deepfake produces fake videos and images, which are difficult to distinguish between authentic images or videos. A free software learning platform has allowed the development of credible faceswaps in videos in recent months, which leaves only a small residue of manipulation in so-called deepfake videos. GANs create deepfakes with two computer models coming out. One is trained on the dataset, and the other aims to explain deepfakes faults. Forger produces fakes until another model does not identify forgery. Deepfakes create fake news that can contribute to financial and social fraud, videos, photographs, and terrorism. Religions, organizations, citizens and societies, environment, protection and democracy are increasingly being influenced. These realistic fake videos cause political distress, challenging someone, or fake terrorist activities are easily conceived. When deep videos and photographs expand in social media, people disregard the facts. This analysis discussed available datasets, deepfake development, deepfake issues, and fake video detection techniques, including fake video detection using GANs. Besides this, deep learning techniques for deepfake detection detailing are provided in this paper.
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Singh, R., Shrivastava, S., Jatain, A., Bajaj, S.B. (2022). Deepfake Images, Videos Generation, and Detection Techniques Using Deep Learning. In: Agrawal, S., Gupta, K.K., Chan, J.H., Agrawal, J., Gupta, M. (eds) Machine Intelligence and Smart Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-9650-3_39
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