AI RESEARCH
Evolutionary Multi-Objective Fusion of Deepfake Speech Detectors
arXiv CS.LG
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ArXi:2604.01330v1 Announce Type: cross 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.