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Found 6 papers, 2 papers with code
Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes
Can we humans correctly perceive the authenticity of the content of the videos we watch?
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.
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)
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)
