Evolutionary Multi-Objective Fusion of Deepfake Speech Detectors
While deepfake speech detectors built on large self-supervised learning (SSL) models achieve high accuracy, employing standard ensemble fusion to further enhance robustness often results in oversized systems with diminishing returns. To address this, we propose an evolutionary multi-objective score fusion framework that jointly minimizes detection error and system complexity. We explore two encodings optimized by NSGA-II: binary-coded detector selection for score averaging and a real-valued scheme that optimizes detector weights for a weighted sum. Experiments on the ASVspoof 5 dataset with 36 SSL-based detectors show that the obtained Pareto fronts outperform simple averaging and logisti...
| Authors | Vojtěch Staněk, Martin Perešíni, Lukáš Sekanina et al. |
|---|---|
| Primary category | cs.SD |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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