Website-Based Implementation of Convolutional Neural Networks for Real Face and Deepfake Image Detection
The development of artificial intelligence increases the risk of facial image manipulation through deepfake technology that is difficult to distinguish visually. This study aims to implement a Convolutional Neural Network (CNN) based on EfficientNetB0 to classify real and deepfake static facial images and integrate the model into a website application. The study follows the CRISP-DM framework using the FaceForencis++ extracted frames dataset from Kaggle, consisting of 60,000 images with 30,000 real images and 30,000 fake images. The data were divided into 70:15:15 ratios for training, validation, and testing, then processed through 224 x 224 pixel resizing, data augmentation, and facial area cropping using OpenCV Haar Cascade during inference. The model achieved 82.50% validation accuracy and 0.3874 validation loss at the 10th epoch. Decision threshold optimization at 0.78 produced 80.99% accuracy, 80.98% macro F1-score, and 78.56% recall for the fake class on the test data. The model was deployed in a Streamlit application, allowing users to upload facial images and receive detection results directly. The results indicate that the combination of EfficientNetB0 and threshold optimization can support preliminary verification of digital facial image authenticity.