3 papers
cs.LG2026
An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection
Romain Hermary, Nesryne Mejri, Djamila Aouada
Anomaly detection is inherently characterised by severe class imbalance, making the interpretation of evaluation metrics challenging. Although metrics such as AUROC, AUPR, F1-score…
cs.CV2026
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…
cs.CV2025
Removing Geometric Bias in One-Class Anomaly Detection with Adaptive Feature Perturbation
Romain Hermary, Vincent Gaudillière, Abd El Rahman Shabayek +1
One-class anomaly detection aims to detect objects that do not belong to a predefined normal class. In practice training data lack those anomalous samples; hence state-of-the-art m…