Results 21 to 30 of about 6,295 (185)

Multi-attentional Deepfake Detection [PDF]

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
CVPR2021 ...
Zhao, Hanqing, Zhou, Wenbo, Chen, Dongdong, Wei, Tianyi, Zhang, Weiming, Yu, Nenghai   +5 more
openaire   +2 more sources

Deepfake attack prevention using steganography GANs [PDF]

PeerJ Computer Science, 2022
Background Deepfakes are fake images or videos generated by deep learning algorithms. Ongoing progress in deep learning techniques like auto-encoders and generative adversarial networks (GANs) is approaching a level that makes deepfake detection ideally ...
Iram Noreen, Muhammad Shahid Muneer, Saira Gillani   +2 more
doaj   +2 more sources

WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection

, 2020
In recent years, the abuse of a face swap technique called deepfake Deepfake has raised enormous public concerns. So far, a large number of deepfake videos (known as "deepfakes") have been crafted and uploaded to the internet, calling for effective ...
Chang, Minghao, Chen, Jingjing, Jiang, Yu-Gang, Ma, Xingjun, Zi, Bojia   +4 more
core   +1 more source

Artificial intelligence and UK national security: Policy considerations [PDF]

, 2020
RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development ...
Babuta, Alexander, Janjeva, Ardi, Oswald, Marion   +2 more
core   +1 more source

DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis [PDF]

, 2021
Applications of deep learning to synthetic media generation allow the creation of convincing forgeries, called DeepFakes, with limited technical expertise. DeepFake detection is an increasingly active research area.
Bassett, Robert, Lutz, Kevin
core  

Automated Deepfake Detection

, 2021
In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection. This is the first time to employ automated machine learning for deepfake detection. Based on our explored search space, our proposed method achieves competitive prediction accuracy compared to previous methods.
Liu, Ping, Lin, Yuewei, He, Yang, Wei, Yunchao, Zhen, Liangli, Zhou, Joey Tianyi, Goh, Rick Siow Mong, Liu, Jingen   +7 more
openaire   +2 more sources

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

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

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