3 citations · 5 across the 12 of their papers we have counts for
12 papers
When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift
Dat Nguyen, Cosmin Radoi, Romain Hermary +4
Recent advances in generative AI, such as diffusion models and face-swapping tools, have enabled the creation of highly realistic deepfakes, leading to real-world harms including f…
LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection
Dat Nguyen, Enjie Ghorbel, Anis Kacem +2
In this paper, we propose Localized Artifact Attention X (LAA-X), a novel deepfake detection framework that is both robust to high-quality forgeries and capable of generalizing to…
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge
Marcella Astrid, Abdelrahman Shabayek, Djamila Aouada
Batteries are essential for various applications, including electric vehicles and renewable energy storage, making safety and efficiency critical concerns. Anomaly detection in bat…
Audio-Visual Deepfake Detection With Local Temporal Inconsistencies
Marcella Astrid, Enjie Ghorbel, Djamila Aouada
This paper proposes an audio-visual deepfake detection approach that aims to capture fine-grained temporal inconsistencies between audio and visual modalities. To achieve this, bot…
Vulnerability-Aware Spatio-Temporal Learning for Generalizable Deepfake Video Detection
Dat Nguyen, Marcella Astrid, Anis Kacem +2
Detecting deepfake videos is highly challenging given the complexity of characterizing spatio-temporal artifacts. Most existing methods rely on binary classifiers trained using rea…
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…
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…
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…