2 research outputs found
A Comprehensive Survey on Deepfake Methods: Generation, Detection, and Applications
Due to recent advancements in AI and deep learning, several methods and tools for multimedia transformation, known as deepfake, have emerged. A deepfake is a synthetic media where a person's resemblance is used to substitute their presence in an already-existing image or video. Deepfakes have both positive and negative implications. They can be used in politics to simulate events or speeches, in translation to provide natural-sounding translations, in education for virtual experiences, and in entertainment for realistic special effects. The emergence of deepfake face forgery on the internet has raised significant societal concerns. As a result, detecting these forgeries has become an emerging field of research, and many deepfake detection methods have been proposed. This paper has introduced deepfakes and explained the different types of deepfakes that exist. It also explains a summary of various deep fake generation techniques, both traditional and AI detection techniques. Datasets used for deepfake-generating that are freely accessible are emphasized. To further advance the deepfake research field, we aim to provide relevant research findings, identify existing gaps, and propose emerging trends for future study
An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactions
Generative AI (GenAI) has shown remarkable capabilities in generating diverse
and realistic content across different formats like images, videos, and text.
In Generative AI, human involvement is essential, thus HCI literature has
investigated how to effectively create collaborations between humans and GenAI
systems. However, the current literature lacks a comprehensive framework to
better understand Human-GenAI Interactions, as the holistic aspects of
human-centered GenAI systems are rarely analyzed systematically. In this paper,
we present a survey of 291 papers, providing a novel taxonomy and analysis of
Human-GenAI Interactions from both human and Gen-AI perspectives. The
dimensions of design space include 1) Purposes of Using Generative AI, 2)
Feedback from Models to Users, 3) Control from Users to Models, 4) Levels of
Engagement, 5) Application Domains, and 6) Evaluation Strategies. Our work is
also timely at the current development stage of GenAI, where the Human-GenAI
interaction design is of paramount importance. We also highlight challenges and
opportunities to guide the design of Gen-AI systems and interactions towards
the future design of human-centered Generative AI applications