29 citations · 29 across the 4 of their papers we have counts for
4 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…
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…
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies
Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
Due to the rarity of anomalous events, video anomaly detection is typically approached as one-class classification (OCC) problem. Typically in OCC, an autoencoder (AE) is trained t…