3 citations · 5 across the 12 of their papers we have counts for
5 papers · 2 filters
FakeFormer: Efficient Vulnerability-Driven Transformers for Generalisable Deepfake Detection
Dat Nguyen, Marcella Astrid, Enjie Ghorbel +1
Recently, Vision Transformers (ViTs) have achieved unprecedented effectiveness in the general domain of image classification. Nonetheless, these models remain underexplored in the…
Detecting Audio-Visual Deepfakes with Fine-Grained Inconsistencies
Marcella Astrid, Enjie Ghorbel, Djamila Aouada
Existing methods on audio-visual deepfake detection mainly focus on high-level features for modeling inconsistencies between audio and visual data. As a result, these approaches us…
Statistics-aware Audio-visual Deepfake Detector
Marcella Astrid, Enjie Ghorbel, Djamila Aouada
In this paper, we propose an enhanced audio-visual deep detection method. Recent methods in audio-visual deepfake detection mostly assess the synchronization between audio and visu…
Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data
Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
In order to devise an anomaly detection model using only normal training data, an autoencoder (AE) is typically trained to reconstruct the data. As a result, the AE can extract nor…
LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection
Dat Nguyen, Nesryne Mejri, Inder Pal Singh +5
This paper introduces a novel approach for high-quality deepfake detection called Localized Artifact Attention Network (LAA-Net). Existing methods for high-quality deepfake detecti…