Investigating the Evolving Landscape of Deepfake Technology: Generative AI's Role in it's Generation and Detection

Abstract

The world of artificial intelligence is constantly changing, with Generative AI and Large Language Models (LLMs) leading the way in bringing new technological advancements. This paper offers a detailed look at these groundbreaking technologies and how they are shaping the digital world today. We explore the technical aspects of Generative AI and LLMs, explain their unique features, and compare them to traditional AI models.One of the key focuses of our research is the growing issue of DeepFakes—artificial intelligence-generated media that presents a significant challenge in verifying content. We conduct a thorough examination of few deepfake detection techniques out of which we will be implementing and analyzing one of them. Our research implements a framework for Deep Fake Image Detection. The suggested solution utilizes a RESNET-50(Residual Network with 50 layers) and MTCNN (Multi-task Cascaded Convolutional Networks) models for detecting whether the images are real or fake. This study conducts the Hypothesis testing for the proposed solution taking in consideration that the current Deepfake detection algorithms are less effective in detecting highly realistic Deepfakes compared to less sophisticated manipulations. By investigating the convergence of deep learning, neural networks, and sophisticated algorithms, we set the stage for advancements in AI-based content verification.