earticle

영어

A specific area of research in artificial intelligence, known as deep learning (DL) has turned into a strong source for the solutions of complicated issues in computer vision and many more. A real application of that is real and fake faces detection. Detection of is real and fake faces became increasingly important these days with increasing deepfake technology. Fake images pose great dangers to information security, trustworthiness in multimedia content, and even to society's stability. In his proposal the design of a deep learning-based model in VGG-16 architecture to make high accuracy and reliability distinctions between real and fake faces. The performance of the proposed model was evaluated by a number of metrics, including accuracy, specificity, recall, precision, and misclassification rate. The results showed that the model obtained an excellent accuracy of 99.61% with a very low misclassification rate of 0.39%. It obtained perfect specificity of 99.15%, which means all fake faces were identified correctly, and a precision value of 99.29%, ensuring that all faces classified as real were indeed real. The recall of the model was high, at 100%, meaning nearly all real faces were correctly identified. The obtained results are the proof of how effective DL and, in this case, using a pre-trained model like VGG-16, is at recognizing real and fake faces. It shows how strong and reliable the proposed.