Results 51 to 60 of about 6,771 (199)

The Deepfake Challenges and Deepfake Video Detection

International Journal of Innovative Technology and Exploring Engineering, 2020
Deepfake is a combination of fake and deep-learning technology. Deep learning is the function of artificial intelligence that can be used to create and detect deepfakes. Deepfakes are created using generative adversarial networks, in which two machine learning models exit.
openaire   +1 more source

Tales of Cyberspace and Artificial Intelligence: Diverging Stakeholderships?

Global Policy, EarlyView.
ABSTRACT This article traces the evolution of the Internet from the 1990s to the 2020s and compares it with the development of Artificial Intelligence (AI), particularly following the public launch of ChatGPT in late 2022. It identifies both parallels and divergencies between these two overlapping technological domains, focusing on the growing ...
Johan Eriksson, Giampiero Giacomello
wiley   +1 more source

DeepFaceLab: A simple, flexible and extensible face swapping framework

, 2020
DeepFaceLab is an open-source deepfake system created by \textbf{iperov} for face swapping with more than 3,000 forks and 13,000 stars in Github: it provides an imperative and easy-to-use pipeline for people to use with no comprehensive understanding of ...
Chervoniy, Nikolay, Dpfks, Mr., Facenheim, Carl Shift, Gao, Daiheng, Jiang, Jian, Liu, Kunlin, Marangonda, Sugasa, Perov, Ivan, RP, Luis, Umé, Chris, Wu, Pingyu, Zhang, Sheng, Zhang, Weiming, Zhou, Bo   +13 more
core  

Integrating Audio-Visual Features for Multimodal Deepfake Detection

, 2023
Deepfakes are AI-generated media in which an image or video has been digitally modified. The advancements made in deepfake technology have led to privacy and security issues.
Jia, Shan, Lyu, Siwei, Muppalla, Sneha
core  

FaceGuard: Proactive Deepfake Detection

CoRR, 2021
Existing deepfake-detection methods focus on passive detection, i.e., they detect fake face images via exploiting the artifacts produced during deepfake manipulation. A key limitation of passive detection is that it cannot detect fake faces that are generated by new deepfake generation methods.
Yuankun Yang, Chenyue Liang, Hongyu He, Xiaoyu Cao, Neil Zhenqiang Gong   +4 more
openaire   +2 more sources

Scenario‐Based AI Literacy Scale (SAILS): Evidence for distinct instrumental and critical‐reflective AI skills and their difference from traditional digital skills

British Journal of Educational Technology, EarlyView.
Abstract Given the increasing integration of Artificial Intelligence (AI) into everyday life and professional contexts, it is essential to investigate learners' existing capabilities regarding AI tools to inform possible interventions to equip them with necessary AI skills, but also advance the theoretical frameworks on digital skills measurement and ...
Christian Scheibenzuber, Olga Chernikova, Johanna Vejvoda, Michael Sailer, Frank Fischer   +4 more
wiley   +1 more source

AI Anomaly-Based Deepfake Detection Using Customized Mahalanobis Distance and Head Pose with Facial Landmarks

Applied Sciences
The development of artificial intelligence has inevitably led to the growth of deepfake images, videos, human voices, etc. Deepfake detection is mandatory, especially when used for unethical and illegal purposes.
Cosmina-Mihaela Rosca, Adrian Stancu
doaj   +1 more source

A Comprehensive Overview of Deep Learning for Deepfakes: Generation, Detection, Dataset: A Survey [PDF]

IJCI International Journal of Computers and Information
The rapid evolution of deep learning techniques, particularly through generative adversarial networks (GANs), has enabled the creation of hyper-realistic synthetic media, heightening concerns in domains such as politics, entertainment, and security ...
Eman AbdElfattah, Nader Mahmoud, Hamdy M.Mousa, Ashraf Elsisi   +3 more
doaj   +1 more source

Too good to be true: Synthetic AI faces are more average than real faces and super‐recognizers know it

British Journal of Psychology, EarlyView.
Abstract The AI revolution has produced synthetic faces that often appear more human than photos of real people. We tested whether individual differences in human face recognition ability explain variation in discriminating AI from real faces. Super‐recognizers – people with exceptional ability to recognize human faces (N = 36) – outperformed a typical
James D. Dunn, David White, Clare A. M. Sutherland, Elizabeth J. Miller, Ben A. Steward, Amy Dawel   +5 more
wiley   +1 more source

Learning Self-distilled Features for Facial Deepfake Detection Using Visual Foundation Models: General Results and Demographic Analysis

Journal on Interactive Systems
Modern deepfake techniques produce highly realistic false media content with the potential for spreading harmful information, including fake news and incitements to violence.
Yan Martins Braz Gurevitz Cunha, Bruno Rocha Gomes, José Matheus C. Boaro, Daniel de Sousa Moraes, Antonio José Grandson Busson, Julio Cesar Duarte, Sérgio Colcher   +6 more
doaj   +1 more source