no code implementations • 5 Nov 2023 • Sahibzada Adil Shahzad, Ammarah Hashmi, Yan-Tsung Peng, Yu Tsao, Hsin-Min Wang
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.
no code implementations • 19 Oct 2023 • Ammarah Hashmi, Sahibzada Adil Shahzad, Chia-Wen Lin, Yu Tsao, Hsin-Min Wang
For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset.
1 code implementation • APSIPA ASC 2022 2022 • Sahibzada Adil Shahzad, Ammarah Hashmi, Sarwar Khan, Yan-Tsung Peng, Yu Tsao, Hsin-Min Wang
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)
1 code implementation • Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 2022 • Ammarah Hashmi, Sahibzada Adil Shahzad, Wasim Ahmad, Chia Wen Lin, Yu Tsao, Hsin-Min Wang
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)