Al-Dulaimi, Omar Alfarouk Hadi HasanKurnaz, Sefer2024-07-172024-07-172024Al-Dulaimi, O. A. H. H., Kurnaz, S. (2024). Deep fake image detection based on deep learning using a hybrid CNN-LSTM with machine learning architectures as classifier. HORA 2024 - 6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings. 10.1109/HORA61326.2024.105507289798350394634https://hdl.handle.net/20.500.12939/4749One of the most important and difficult subjects in social communication is detecting deepfake images and videos. Deepfake techniques have developed widely, making this technology quite available and proficient enough so that there is worry about its bad application. Considering this issue, discovering fake faces is very important for ensuring security and preventing sociopolitical issues on a private and general level. Deep learning provides higher performance than typical image processing approaches when it comes to deepfake detection. This work presents construction of an artificial intelligence system, which is capable of detecting deepfake from more than one dataset. This study proposes neural network models based on deep learning using random forest (RF) and support vector machines (SVM) as classifier for deepfake detection. The use of two classifiers (RF) and (SVM) and their combination with a convolutional neural network is the first study of its kind in the field of deepfake detection in images from three open-source datasets (FaceForensics++, FaceAntiSpoofing, and iFakeFaceDB). This proposed method shows an accuracy of 96%, 87% and 52% in iFakeFaceDB, CelebA-Spoof, FaceForensics++ and respectively.eninfo:eu-repo/semantics/closedAccessConvolutional neural networkDeepFake detectionMachine learningConference Object2-s2.0-85196704354N/A