Amid growing concerns over the proliferation of deepfakes and AI-generated visual content, there is an urgent need for robust and interpretable detection systems to safeguard authenticity, trust, and digital integrity. In the current research, we provide a comprehensive Two-stage AI-powered image detection system programmed to differentiate between real images and synthetically generated ones with high accuracy and interpretability. The first stage applies a binary classifier built upon the EfficientNet-B3 backbone, combined with a self-attention for enhancing world spatial awareness and classifying images as real or computer-generated. On recognizing an image as artificial, the second phase is invoked, performing a fine-grained feature processing with ten specialized heads of neurons—each focused on specific AI image artifacts like texture inconsistencies, anomalies at the distortions, artificial light, and geometric deformations. This interpretability-driven design includes ResNet50 alongside the Vision Transformer (ViT) Models within Auxiliary Modules for Augmentation context-aware feature extraction and analysis. The system achieved a binary classification accuracy of 95.5%, with an ROC-AUC of 0.990 and a precision-recall AUC of 0.988. In the artifact scoring phase, AI-generated images consistently showed feature scores ranging from 70% to 100%, while authentic images fell within a 0% to 30% range, confirming the system’s discriminative power. Designed with scalability and transparency in mind, this tool is well-suited for deployment by journalists, digital forensics teams, researchers, and the general public to verify image authenticity and combat misinformation. Future enhancements include expanding the model to video-based deepfake detection, adapting it to emerging generative architectures, and integrating it into real-time applications for web-based platforms.