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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.1
- 수록면
- 69 - 81 (13page)
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In the following years, technology has progressed in so many ways that it has provided the cyber society with a resource that only computers can excel at, such as the art of counterfeit of media, which was before unavailable. Deepfakes are a term used to describe this kind of deception. The majority of well-documented Deep Fakes are produced using Generative Adversarial Network (GAN) Models, which are essentially two distinct Machine Learning Models that perform the roles of attack and defence. These models create and identify deepfakes until they reach a point where the morphing no longer detects the deepfakes anymore. Using this algorithm/model, it is possible to discover and create new media that has a similar demographic to the training set, resulting in the development of the ideal Deep Fake media. Because the alterations are carried out utilising advanced characteristics, they cannot be seen with the human eye. However, it is completely feasible to develop an algorithm that can automatically identify this kind of tampering carried out via the internet. This not only enables us to broaden the scope of our search beyond a single media item, but also beyond a large library of mixed media. The more it learns, the better it becomes as artificial intelligence takes over in full force with automation. In order to create better deep fakes, new models are being developed all the time, making it more difficult to distinguish between genuine and morphing material.
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참고문헌 (46)
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Eali Stephen Neal Joshua / 2021 / 3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation / Journal of Healthcare Engineering 2021:11
Joshua, E. S. N. / 2020 / An extensive review on lung cancer detection using machine learning techniques : A systematic study / Revue d'Intelligence Artificielle 34(3):351~359
Park, T / 2019 / Semantic Image synthesis with spatially-adaptive normalization / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
DeepFaceLab:Explained And Usage Tutorial
Eali Stephen Neal Joshua / 2021 / 3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation / Journal of Healthcare Engineering 2021:11
Joshua, E. S. N. / 2020 / An extensive review on lung cancer detection using machine learning techniques : A systematic study / Revue d'Intelligence Artificielle 34(3):351~359
Park, T / 2019 / Semantic Image synthesis with spatially-adaptive normalization / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
DeepFaceLab:Explained And Usage Tutorial
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