7 papers · 1 filter
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…
Targeted Augmented Data for Audio Deepfake Detection
Marcella Astrid, Enjie Ghorbel, Djamila Aouada
The availability of highly convincing audio deepfake generators highlights the need for designing robust audio deepfake detectors. Existing works often rely solely on real and fake…
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…
Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies
Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada +1
Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or repr…