Fake vs. Real Face Discrimination Using Convolutional Neural Networks

The rapid advancements in image manipulation technology and the proliferation of Generative Adversarial Network (GAN) generated content have created a pressing need for effective methods to distinguish between fake and real imagery. Convolutional Neural Networks (CNNs) have exhibited exceptional performance in image recognition tasks, making them an ideal choice for addressing the challenge of fake vs. real face discrimination. This paper proposes three new CNN models specifically designed for discriminating between fake and real facial images. The proposed models are compared with several state-of-the-art pre-trained models, such as Inception-ResNet-V2. An extensive dataset made up of both real and fake face images is chosen to enable thorough inspection. Extensive experiments are conducted to evaluate the performance of the proposed models and compare them with the selected pre-trained models. The evaluation metrics employed include Accuracy, Precision, Recall, F1-score, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Receiver Operating Characteristic (ROC), and Confusion Matrix. These metrics provide a comprehensive analysis of the model’s capability to accurately classify fake and real faces. The results demonstrate that Model 3 achieves highly promising performance in discriminating between fake and real faces. The comparison with pre-trained models reveals the superiority of Model 3 in terms of testing results, discrimination ability on new data, training, and validation performances. This study contributes to the field of fake vs. real face discrimination. The findings hold significant implications for applications in areas such as forensic analysis and social media content moderation.