This tutorial provides a detailed, step-by-step guide to understanding, creating, and detecting deepfakes using both GPU and CPU resources. Starting with an overview of deepfake technology, learners gain insight into how synthetic videos and images are generated using deep learning techniques.

The course covers the practical setup required for running deepfake models on different hardware configurations, including optimizing training and inference for GPU and CPU environments. Key models such as autoencoders, GANs, and face-swapping networks are explained in a hands-on manner.

Participants will learn to manipulate datasets, preprocess images, train deepfake models, and evaluate outputs for quality and realism. The tutorial also emphasizes ethical considerations and responsible use of deepfake technology, including how to detect manipulated media using CNN-based detection networks like MesoNet.

By the end of this tutorial, learners will have a solid understanding of the deepfake workflow, from data preparation to model training and evaluation. This course is ideal for AI enthusiasts, students, and professionals looking to explore computer vision, generative models, and digital media authenticity.