Detecting Deepfakes Using Convolutional Neural Networks

Abstract

Abstract Deepfakes are synthetic media generated using artificial intelligence, particularly deep learning techniques such as Generative Adversarial Networks (GANs). As these media become more realistic, the need for effective detection mechanisms has become critical. This paper presents a method for detecting deepfake videos and images using Convolutional Neural Networks (CNNs). A custom CNN model is proposed and evaluated on a publicly available deepfake dataset. Results demonstrate that CNNs can effectively learn spatial inconsistencies and subtle artifacts left by generative models. The paper also discusses performance metrics, comparative studies, and future directions in deepfake detection using deep learning