AI-Powered Deepfake Detection

Abstract

This research work presents a method that utilizes minimal training data and time to generate customized, photo- realistic talking head models. The technique employs few-shot learning, enabling the generation of satisfactory results from a single image, with improved fidelity using additional inputs. Unlike traditional warping-based approaches, the system synthesizes video frames directly using deep convolutional networks. The learning process is defined through adversarial training involving high-capacity generators and discriminators, which allows the system to quickly adapt to new identities through extensive meta-learning on large-scale video datasets. A person- specific parameter initialization further accelerates training and enhances performance. The proposed approach demonstrates the capability to produce lifelike talking head models of previously unseen individuals, including those depicted in portrait paintings. The paper discusses the ability of deepfake technology to produce artificial intelligence-generated digital content that looks real is examined closely. This research takes into account the wider societal ramifications as well as the complexities of AI algorithms in creating and detecting deepfakes. It highlights the critical requirement for advanced detection systems to stop exploitation and considers the continuous development of this powerful technology.