Deepfake Detection Based on Deep Learning and Quantum Particle Swarm Optimization - Ebeid Ali Ebeid; Ahmed Sobhi; H. A. El Shenbary (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2026)
Recently, many fake images and videos can be created and manipulated easily by employing Artificial Intelligence technologies, mainly based on Generative Adversarial Network (GAN). These synthesized media (images or videos) can be used later in unethical ways to create panic among individuals. This type of technology, which synthesizes human images and videos, is called deepfake. This paper proposes a deepfake detection methodology relying on Deep Learning (DL) approach and quantum particle swarm optimization (QPSO). The input image undergoes preprocessing by choosing the Region of Interest (ROI), followed by the application of an image downsampling algorithm to minimize the dimension of the new sub-image and thus decrease processing time costs. After that, the image has been normalized. The optimal subset of dataset has been determined by adapting QPSO during training phase. To perform classification of images and videos, ResNet50 model accomplishes this task perfectly. Experiments indicate that the suggested algorithm demonstrates a significant level of accuracy on ”Real and Fake Face Detection ” and ” Celeb-DF” datasets. The proposed approach achieves accuracy 99.6 % on Celeb-DF and 99.8 % on Real and Fake Face Detection dataset.