Deepfake Image Classification Using ResNet50 Feature Extraction and XGBoost Learning Model

Deepfake is an artificial intelligence-based media manipulation technology that realistically fabricates a person's face, voice, and movements in both video and audio formats. The increasing use of deepfakes in the creation of various forms of deceptive content, including pornography, fake news, and fraud, has led to an urgent need for effective detection methods. One of the main challenges in detecting deepfakes is the high quality and realism of synthetic media, which renders conventional detection techniques less effective. Therefore, machine learning techniques capable of recognizing subtle patterns in visual data that are imperceptible to the human eye are required. This study aims to develop a deepfake image detection system using a hybrid machine learning approach that combines ResNet50 for feature extraction and XGBoost for classification. The pre-trained ResNet50 model, originally trained on the large-scale ImageNet dataset, is utilized to extract visual representations from images in the form of feature vectors. These features are then classified using XGBoost to distinguish between authentic and AI-generated images based on subtle patterns embedded within the extracted features. The results demonstrate that this hybrid approach achieves an accuracy of 94.6% in detecting deepfake images by leveraging the deep representation power of CNNs and the advanced classification capabilities of XGBoost. This method is not only computationally efficient but also highly relevant for integration into adaptive digital security systems.