Browsing by Author "Aldalbahi, Bedour Ahmad"

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Deepfake Face Images Detection
(Bahrain Polytechnic, 2024) Aldalbahi, Bedour Ahmad; Fawzy, Abdelhameed Ibrahim
Deepfake is a sort of AI that forges original image or video and create persuading images, audio and video. Deepfake media continues to gain ground online, raising a number of ethical and moral questions about their use, in that deepfakes can be used to undermine political elections, companies, individual and corporate finances, reputation, and many more. The proposed system to solve this problem is to use the most popular algorithm in deep learning, Convolution Neural Network (CNN), for detecting fake images. This will be achieved by training two deep learning models and analyzing their performances in distinguishing between the two classes of images “Real”,” Fake”. Our main aim is to contribute a useful framework toward the detection of deep-fake photos with deep learning. This thesis proposed convolutional neural networks for the identification of genuine and deepfake pictures. In this study, we have trained two models: DenseNet121 and ResNet50. The results will be categorized by Four evaluation metrics: accuracy, precision, recall, and F1-score. In that respect, DenseNet121 had the best performance with an accuracy of 94%. Besides, we obtained 91% from the ResNet50.
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