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Thambawita, V., Isaksen, J., Hicks, S., Ghouse, J., Ahlberg, G., Linneberg, A., Grarup, N., Ellervik, C. and Olesen, M. (2021) Deepfake Electrocardiograms Using Generative Adversarial Networks Are the Beginning of the End for Privacy Medicine. Scientific Reports, 11, 21869-21889.
https://doi.org/10.1038/s41598-021-01248-9

has been cited by the following article:

• TITLE: Explainable Deep Fake Framework for Images Creation and Classification

  AUTHORS: Majed M. Alwateer

  KEYWORDS: Deepfakes, Machine Learning, Deep Learning Fake Detection, Social Media LIME Technique

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.5, May 22, 2024

  ABSTRACT: Deep learning is a practical and efficient technique that has been used extensively in many domains. Using deep learning technology, deepfakes create fake images of a person that people cannot distinguish from the real one. Recently, many researchers have focused on understanding how deepkakes work and detecting using deep learning approaches. This paper introduces an explainable deepfake framework for images creation and classification. The framework consists of three main parts: the first approach is called Instant ID which is used to create deepfacke images from the original one; the second approach called Xception classifies the real and deepfake images; the third approach called Local Interpretable Model (LIME) provides a method for interpreting the predictions of any machine learning model in a local and interpretable manner. Our study proposes deepfake approach that achieves 100% precision and 100% accuracy for deepfake creation and classification. Furthermore, the results highlight the superior performance of the proposed model in deep fake creation and classification.