Recent advances in generative modeling have significantly increased the realism of synthetic facial images, making automatic deepfake detection an important task. This paper proposes a hierarchical and explainable framework for deepfake face image analysis. In the first stage, a binary classifier distinguishes authentic images from manipulated ones. In the second stage, images predicted as fake are further assigned to one of three manipulation subtypes: diffusion-based synthesis, identity face swapping using SimSwap, and fully synthetic face generation using StyleGAN2. The framework is implemented using convolutional neural network (CNN) backbones initialized from ImageNet-pretrained weights and is evaluated on a custom dataset containing authentic and manipulated facial images. To improve interpretability, the proposed pipeline is complemented with Grad-CAM++ and SHAP-based visual explanations, allowing the analysis of the image regions that contribute most strongly to model decisions. The proposed approach is intended to support both authenticity detection and manipulation-source attribution within a unified experimental setting.