Abstract
In recent years, there have been booming improvements in the fields of deep learning and artificial intelligence (AI). However, the growth also has resulted in a new age of realistic digital manipulation of videos known as deepfakes. With this new research, it is difficult to differentiate between real and manipulated media. The growing popularity of deepfakes and easy availability of open source creating tools have become a concern of privacy and a threat to society. This has led to the need to develop an accurate and efficient tool that can identify and stop the harm these media might cause. Recently, there has been a lot of exploration and research for identifying these synthetic media. Meanwhile, there is a lot of focus to find the best feature scales for detection and improving the existing algorithms. Features and attributes such as background comparison, eye blinking patterns, facial artifacts, and pose estimation are currently being used. The proposed Hybrid Model for Deepfake Detection (HMDD) which is aimed to achieve better performance in detecting.
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Rajesh, N., Prajwala, M.S., Kumari, N., Rayyan, M., Ramachandra, A.C. (2022). Hybrid Model for Deepfake Detection. In: Tomar, A., Malik, H., Kumar, P., Iqbal, A. (eds) Proceedings of 3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication. Lecture Notes in Electrical Engineering, vol 915. Springer, Singapore. https://doi.org/10.1007/978-981-19-2828-4_57
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DOI: https://doi.org/10.1007/978-981-19-2828-4_57
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