Results 31 to 40 of about 6,771 (199)

FaceSwap based DeepFakes Detection

The International Arab Journal of Information Technology, 2022
The progression of Machine Learning (ML) has introduced new trends in the area of image processing. Moreover, ML presents lightweight applications capable of running with minimum computational resources like Deepfakes, which generates widely manipulated multimedia data.
Marriam Nawaz, Momina Masood, Ali Javed, Tahira Nazir   +3 more
openaire   +1 more source

Detection of Frauds in Deep Fake Using Deep Learning

Engineering Proceedings
Research on DeepFake detection using deep neural networks (DNNs) has gained more attention in an effort to detect and categorize DeepFakes. In essence, DeepFakes are regenerated content made by changing particular DNN model elements.
Osipilli Aparna, Pakanati Rani, Tulluri Ramya, Tanneru Priyanka, Neela Sundari, P. G. K. Sirisha, Repudi Ramesh, Dama Anand   +7 more
doaj   +1 more source

A Critical Review of the 2025 RSHE Guidance and Alternative Approach Framed in Safe Uncertainty

Children &Society, EarlyView.
ABSTRACT This policy review critically examines the English government's 2025 statutory guidance on Relationships Education, Relationship and Sex Education and Health Education (RSHE), analysing its educational assumptions, strengths and limitations through the lens of safe uncertainty.
Emily Setty, Jonny Hunt
wiley   +2 more sources

Improving Video Vision Transformer for Deepfake Video Detection Using Facial Landmark, Depthwise Separable Convolution and Self Attention

IEEE Access
In this paper, we present our result of research in video deepfake detection. We built a deepfake detection system to detect whether a video is a deepfake or real. The deepfake detection algorithm still struggle in providing a sufficient accuracy values,
Kurniawan Nur Ramadhani, Rinaldi Munir, Nugraha Priya Utama   +2 more
doaj   +1 more source

Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media [PDF]

PeerJ Computer Science
The rapid advancement of deepfake technology poses an escalating threat of misinformation and fraud enabled by manipulated media. Despite the risks, a comprehensive understanding of deepfake detection techniques has not materialized.
Shavez Mushtaq Qureshi, Atif Saeed, Sultan H. Almotiri, Farooq Ahmad, Mohammed A. Al Ghamdi   +4 more
doaj   +2 more sources

A Lightweight Reliably Quantified Deepfake Detection Approach [PDF]

, 2022
Deepfake has brought huge threats to society such that everyone can become a potential victim. Current Deepfake detection approaches have unsatisfactory performance in either accuracy or efficiency.
Chow, Kam Pui, Wang, Tianyi
core   +1 more source

Dual-Channel Deepfake Audio Detection: Leveraging Direct and Reverberant Waveforms

IEEE Access
Deepfake content-including audio, video, images, and text-synthesized or modified using artificial intelligence is designed to convincingly mimic real content.
Gunwoo Lee, Jungmin Lee, Minkyo Jung, Joseph Lee, Kihun Hong, Souhwan Jung, Yoseob Han   +6 more
doaj   +1 more source

Improved Optical Flow Estimation Method for Deepfake Videos

Sensors, 2022
Creating deepfake multimedia, and especially deepfake videos, has become much easier these days due to the availability of deepfake tools and the virtually unlimited numbers of face images found online.
Ali Bou Nassif, Qassim Nasir, Manar Abu Talib, Omar Mohamed Gouda   +3 more
doaj   +1 more source

Why Are Consumers Ambivalent About AI‐Generated Images? The Moderating Role of Commercial Versus Noncommercial Content Type

Journal of Consumer Behaviour, EarlyView.
ABSTRACT Grounded in ambivalence theories, this research examined factors shaping consumer ambivalence toward AI‐generated content and investigated differences between commercial and noncommercial contexts. As a preliminary study, sentiment analysis of Reddit data using a support vector machine (SVM) revealed that most consumer sentiment toward AI ...
Garim Lee, Jinsu Park, Hye‐Young Kim, Divya Manoj   +3 more
wiley   +1 more source

Design and development of an efficient RLNet prediction model for deepfake video detection

Frontiers in Big Data
IntroductionThe widespread emergence of deepfake videos presents substantial challenges to the security and authenticity of digital content, necessitating robust detection methods.
Varad Bhandarkawthekar, T. M. Navamani, Rishabh Sharma, K. Shyamala   +3 more
doaj   +1 more source