Abstract
Deepfake algorithms can make forged pictures and videos that people cannot differentiate them from true ones. The suggestion of technology that locate and prove the truth of virtual visual media is as a result essential. Deepfakes generates 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 survey of tools and algorithms used to make deepfakes and, additional significantly, methods to locate deepfakes. We present huge discussions on challenges, studies, advances and strategies associated to deepfake. By reviewing the history of deepfakes and cutting-edge deepfake detection strategies, this gives a comprehensive assessment of deepfake techniques and helps the development of latest and more robust strategies to deal with an increasing number of tough deepfakes.
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Deshmukh, A., Wankhade, S.B. (2021). Deepfake Detection Approaches Using Deep Learning: A Systematic Review. In: Balas, V.E., Semwal, V.B., Khandare, A., Patil, M. (eds) Intelligent Computing and Networking. Lecture Notes in Networks and Systems, vol 146. Springer, Singapore. https://doi.org/10.1007/978-981-15-7421-4_27
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DOI: https://doi.org/10.1007/978-981-15-7421-4_27
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