Hybrid DCGAN-ResNet50 Model for Fake Face Detection

The increasing technology of digital editing tools has made it harder for humans to distinguish between real and fake faces. Although they can be used in positive applications such as in movies, virtual assistants, video games, and creative arts, some people are using these fake faces for identity fraud, non-consensual content creation, and spreading false information. A primary technology used for generating fake faces is the Generative Adversarial Network (GAN). GANs generate fake faces using adversarial losses between generator and discriminator networks. To minimize this issue, deep learning techniques are being used for distinguishing real and fake faces, achieving more consistent and accurate results. This study introduces a hybrid model that combines the generative strength of Deep Convolutional Generative Adversarial Networks (DCGAN) and the discriminative ability of RESNET50, where DCGAN is one of the advanced GAN technologies to generate new fake faces and RESNET50 is one of the deep convolutional neural networks to classify between the fake faces and real faces. The proposed hybrid model achieves strong performance on the face images dataset, resulting in a precision of 0.91922, a recall of 0.9649, an accuracy of 0.93314, and an ROC under AUC score of 0.986. With the ability to accurately differentiate between real and fake faces, these technologies can help prevent identity fraud, reduce the spread of misinformation, and protect sensitive information from unauthorized access.