Search Results for author:
Found 5 papers, 2 papers with code
CapST: An Enhanced and Lightweight Model Attribution Approach for Synthetic Videos
Deepfake videos, generated through AI faceswapping techniques, have garnered considerable attention due to their potential for powerful impersonation attacks.
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection
This study proposes a new method based on a multi-modal self-supervised-learning (SSL) feature extractor to exploit inconsistency between audio and visual modalities for multi-modal video forgery detection.
AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection
For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset.
Multimodal Forgery Detection Using Ensemble Learning
The recent rapid revolution in Artificial Intelligence (AI) technology has enabled the creation of hyper-realistic deepfakes, and detecting deepfake videos (also known as AIsynthesized videos) has become a critical task.
Ranked #1 on
Multimodal Forgery Detection
on FakeAVCeleb
(using extra training data)
Lip Sync Matters: A Novel Multimodal Forgery Detector
Deepfake technology has advanced a lot, but it is a double-sided sword for the community.
Ranked #1 on
DeepFake Detection
on FakeAVCeleb
(Accuracy (%) metric)
