Study of Deep Fake in Images and Videos using Convolutional Neural Network

In today's rapidly evolving digital landscape, advancements in AI and machine learning (ML) are transforming various industries. One of the most notable solicitations of ML is deepfake technology, which relies on deep learning, specialized subset of Machine Learning. Deepfakes enable the manipulating videos and images by swapping one person's face with another's. While some alterations are easily detectable, others can be much more difficult to identify, depending on the precision of the algorithms used. With the rise of generative adversarial networks (GANs), it has become increasingly easy to create highly realistic fake images and videos, leading to an increase in deceptive media circulating online. These forged visuals serve a range of purposes, from entertainment and financial gain to spreading misinformation or advancing political agendas. Because of their lifelike quality, deepfakes can easily mislead viewers. Recent studies have highlighted the growing impact of deepfake media on society, organizations, and communities. This paper investigates methods for detecting fake videos and images using Convolutional Neural Networks (CNNs), aiming to develop a model that can effectively distinguish between genuine and manipulated content by training on both real and fake datasets.