Results 41 to 50 of about 6,210 (184)

A Comprehensive Analysis of AI Biases in DeepFake Detection With Massively Annotated Databases

, 2023
In recent years, image and video manipulations with Deepfake have become a severe concern for security and society. Many detection models and datasets have been proposed to detect Deepfake data reliably.
Pedersen, Marius, Raja, Kiran, Terhörst, Philipp, Xu, Ying   +3 more
core  

People have different expectations for their own versus others' use of AI‐mediated communication tools

British Journal of Psychology, EarlyView.
Abstract Artificial intelligence (AI) can enhance human communication, for example, by improving the quality of our writing, voice or appearance. However, AI mediated communication also has risks—it may increase deception, compromise authenticity or yield widespread mistrust. As a result, both policymakers and technology firms are developing approaches
Zoe A. Purcell, Mengchen Dong, Anne‐Marie Nussberger, Nils Köbis, Maurice Jakesch   +4 more
wiley   +1 more source

Deepfake video detection methods, approaches, and challenges

Alexandria Engineering Journal
Deepfake technology creates highly realistic manipulated videos using deep learning models, which makes distinguishing between authentic and fake content extremely difficult.
Mubarak Alrashoud
doaj   +1 more source

Deepfakes Generation and Detection: A Short Survey

Journal of Imaging, 2023
Advancements in deep learning techniques and the availability of free, large databases have made it possible, even for non-technical people, to either manipulate or generate realistic facial samples for both benign and malicious purposes. DeepFakes refer
Zahid Akhtar
doaj   +1 more source

Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?

, 2023
Advances in Large Language Models (e.g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts.
Huang, Ting-Hao 'Kenneth', Le, Thai, Lee, Dongwon, Lee, Jooyoung, Shen, Hua, Uchendu, Adaku   +5 more
core  

Deepfake Image Detection

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
The growth of deepfakes in today’s digital environ- ment raises significant doubts regarding the genuineness and dependability of the content found. To overcome this new challenge, Developing an effective method in the context of detection of deep images.
V. Shinde Swati, Madhura Abnave, Vishakha Despande, Atharv Chougule, Pratiksha Darekar   +4 more
openaire   +2 more sources

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

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  

Compassionate Digital Innovation: A Pluralistic Perspective and Research Agenda

Information Systems Journal, EarlyView.
ABSTRACT Digital innovation offers significant societal, economic and environmental benefits but is also a source of profound harms. Prior information systems (IS) research has often overlooked the ethical tensions involved, framing harms as ‘unintended consequences’ rather than symptoms of deeper systemic problems.
Raffaele F. Ciriello, Lars Mathiassen, Marten Risius, Marc Cheong, Helana Scheepers, Emmanuelle Vaast   +5 more
wiley   +1 more source