Results 91 to 100 of about 6,295 (185)
Face Forgery Detection and Attribution via Prototype Disentanglement [PDF]
The detection and attribution of face forgery aims to determine whether a face in an image or video has been manipulated or synthesized using Deepfake techniques, as well as to further analyze the Deepfake method behind it.
QIAN Fei, LI Wei, CHEN Peng, CHEN Haoran, XIE Lipeng, LIU Liyuan
doaj +1 more source
MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection
Deepfakes are synthetic media generated using deep generative algorithms and have posed a severe societal and political threat. Apart from facial manipulation and synthetic voice, recently, a novel kind of deepfakes has emerged with either audio or ...
Katamneni, Vinaya Sree, Rattani, Ajita
core
EEG-features for generalized deepfake detection [PDF]
Since the advent of Deepfakes in digital media, the development of robust and reliable detection mechanism is urgently called for. In this study, we explore a novel approach to Deepfake detection by utilizing electroencephalography (EEG) measured from ...
Beckmann, A. +10 more
core +1 more source
An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
Video deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation.
Sarah Tipper +2 more
doaj +1 more source
Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models
Parallel to the development of advanced deepfake audio generation, audio deepfake detection has also seen significant progress. However, a standardized and comprehensive benchmark is still missing. To address this, we introduce Speech DeepFake (DF) Arena,
Sandipana Dowerah +9 more
doaj +1 more source
ABSTRACT Deepfake detection systems have become critical in combating the growing misuse of synthetic media, which leverages advanced AI techniques to manipulate video, audio, and images. These systems aim to identify and differentiate genuine content from altered or artificially generated media by employing various machine learning and deep ...
openaire +1 more source
Neural Network Ensemble Method for Deepfake Classification Using Golden Frame Selection
Deepfake technology poses significant threats in various domains, including politics, cybersecurity, and social media. This study uses the golden frame selection technique to present a neural network ensemble method for deepfake classification.
Khrystyna Lipianina-Honcharenko +4 more
doaj +1 more source
A Comprehensive Evaluation of Deepfake Detection Methods: Approaches, Challenges and Future Prospects [PDF]
Advances in technology have made deepfake forgeries easier, posing serious ethical and security risks that highlight the urgent need for better detection methods.
Hu Xixi
doaj +1 more source
DeepFake-o-meter v2.0: An Open Platform for DeepFake Detection
Deepfakes, as AI-generated media, have increasingly threatened media integrity and personal privacy with realistic yet fake digital content. In this work, we introduce an open-source and user-friendly online platform, DeepFake-O-Meter v2.0, that integrates state-of-the-art methods for detecting Deepfake images, videos, and audio.
Ju, Yan +9 more
openaire +2 more sources
Deepfake detection: critical review of state-of-the-art approaches and future perspectives
In recent years, the advancement of technology, particularly in domains such as AI, machine learning, and deep learning, has fostered the development of novel tools for altering visual media, including images, audios, and videos.
B. C. Soundarya, H. L. Gururaj
doaj +1 more source

