Navigating the ethics and legality of deepfake technology: advancements, implications and responsible deployment

Abstract

This research explores the world of deepfake technology, particularly how generative adversarial networks can be used to create realistic synthetic images of celebrities. By using the CelebA dataset with a deep convolutional generative adversarial network (DCGAN) model, the research aims to produce high-quality deepfake images that look convincing. The objectives include producing convincing deepfake visuals, examining their practical uses in art and science, ensuring adherence to legal and ethical standards and increasing public awareness of responsible deepfake practices. Thorough testing is conducted to evaluate how well these deepfakes perform and how realistic they appear, while also considering the ethical issues and risks of misuse. The findings show that DCGANs are effective at replicating facial features and emotions, suggesting future possibilities for deepfake technology while considering the need for strong ethical guidelines and regulations