Results 121 to 130 of about 6,210 (184)

Deepfake Detection Using XceptionNet

International Journal of Scientific Research in Science and Technology
The rapid rise of synthetic media, especially deepfakes, has sparked major concerns around misinformation, identity fraud, and diminishing public confidence in visual content. As these altered videos grow increasingly realistic, there is a pressing demand for reliable and scalable detection methods.
null Muskan Kumari, null Sanya Gupta, null Neha Singh   +2 more
openaire   +1 more source

VIOLA jones algorithm with capsule graph network for deepfake detection. [PDF]

PeerJ Comput Sci, 2023
K V, Trojovský P, Hubálovský Š.
europepmc   +1 more source

Cooperative edge deepfake detection

, 2021
Deepfakes are an emerging problem in social media and for celebrities and political profiles, it can be devastating to their reputation if the technology ends up in the wrong hands. Creating deepfakes is becoming increasingly easy. Attempts have been made at detecting whether a face in an image is real or not but training these machine learning models ...
Hasanaj, Enis, Aveler, Albert, Söder, William   +2 more
openaire   +1 more source

Evaluation of deepfake detection using YOLO with local binary pattern histogram. [PDF]

PeerJ Comput Sci, 2022
Hubálovský Š, Trojovský P, Bacanin N, K V.   +3 more
europepmc   +1 more source

Deepfake detection by human crowds, machines, and machine-informed crowds. [PDF]

Proc Natl Acad Sci U S A, 2022
Groh M, Epstein Z, Firestone C, Picard R.   +3 more
europepmc   +1 more source

Deepfake Detection

SSRN Electronic Journal
Sharv Sankpal, Junaid Kazi, Man Kadu, Avadhoot Jogade, Yuvraj Nikam   +4 more
openaire   +2 more sources

Process-Driven Modelling of Media Forensic Investigations-Considerations on the Example of DeepFake Detection. [PDF]

Sensors (Basel), 2022
Kraetzer C, Siegel D, Seidlitz S, Dittmann J.   +3 more
europepmc   +1 more source

Deepfake tweets automatic detection

This study addresses the critical challenge of detecting DeepFake tweets by leveraging advanced natural language processing (NLP) techniques to distinguish between genuine and AI-generated texts. Given the increasing prevalence of misinformation, our research utilizes the TweepFake dataset to train and evaluate various machine learning models.
Frej, Adam, Kaminski, Adrian, Marciniak, Piotr, Szmajdzinski, Szymon, Kuntur, Soveatin, Wroblewska, Anna   +5 more
openaire   +2 more sources

MFF-Net: Deepfake Detection Network Based on Multi-Feature Fusion. [PDF]

Entropy (Basel), 2021
Zhao L, Zhang M, Ding H, Cui X.
europepmc   +1 more source

Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features. [PDF]

J Imaging, 2021
Siegel D, Kraetzer C, Seidlitz S, Dittmann J.   +3 more
europepmc   +1 more source