no code implementations • 7 Nov 2023 • Wasim Ahmad, Yan-Tsung Peng, Yuan-Hao Chang, Gaddisa Olani Ganfure, Sarwar Khan, Sahibzada Adil Shahzad
Deepfake videos, generated through AI faceswapping techniques, have garnered considerable attention due to their potential for powerful impersonation attacks.
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 • 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)
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)