Deepfake Image Detection Using Transfer Learning

The proliferation of DeepFake technology, powered by advancements in generative models such as Generative Adversarial Networks (GANs), poses serious threats to digital authenticity, privacy, and information integrity. In response to this growing concern, this paper presents a DeepFake Detection System implemented as a Flask web application utilizing deep learning techniques. The core of the detection mechanism is a fine-tuned ResNet50 convolutional neural network, pre-trained on ImageNet and adapted through transfer learning to accurately classify real and fake images. The system provides a user-friendly interface allowing registered users to upload images for real-time DeepFake detection, delivering immediate classification results. The application also includes distinct admin and user functionalities_x0097_admins can manage users and maintain a FAQ section, while users can register, log in, and access the detection and informational features. Powered by PyTorch for backend model processing and Flask for web deployment, the system offers a practical tool for media verification in domains such as journalism, security, and social media, contributing to the global fight against misinformation and synthetic media manipulation.