DEEPGUARD: A Deep Learning Framework for Detecting Synthetic Media Using CNN-Based Classification and Visual Differentiation

The proliferation of deepfake technology poses significant risks to media authenticity, trust, and cybersecurity. In this paper we present DEEPGUARD, a deep learning-based framework. It focuses on identifying images generated by generative adversarial networks (GANs). The proposed model is built using convolutional neural networks (CNNs). Both the Xception model and custom-designed CNN architectures are used to extract features that help distinguish between authentic and manipulated facial images. The detection system was trained and evaluated using a curated deepfake dataset and real facial images. DEEPGUARD also displays real and fake images next to each other, helping to highlight spatial inconsistencies for better interpretability. The model achieves high classification accuracy and demonstrates robustness against a range of deepfake artifacts. Experimental results affirm the system’s potential as a scalable, explainable, and reliable approach for synthetic media verification in digital forensics and social media monitoring.