# Evolutionary Multi-Objective Fusion of Deepfake Speech Detectors

arXiv: https://arxiv.org/abs/2604.01330 · HTML: https://mlsanity.com/paper/2604.01330/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale](https://mlsanity.com/paper/2408.08739/) (2024)
- [Graph Attention Networks for Anti-Spoofing](https://mlsanity.com/paper/2104.03654/) (2021)
- [An attention-based backend allowing efficient fine-tuning of transformer models for speaker verification](https://mlsanity.com/paper/2210.01273/) (2022)
- [WavLM model ensemble for audio deepfake detection](https://mlsanity.com/paper/2408.07414/) (2024)
- [BUT Systems and Analyses for the ASVspoof 5 Challenge](https://mlsanity.com/paper/2408.11152/) (2024)
- [Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection](https://mlsanity.com/paper/2409.05032/) (2024)
- [USTC-KXDIGIT System Description for ASVspoof5 Challenge](https://mlsanity.com/paper/2409.01695/) (2024)
- [SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis](https://mlsanity.com/paper/2508.07944/) (2025)
- [SZU-AFS Antispoofing System for the ASVspoof 5 Challenge](https://mlsanity.com/paper/2408.09933/) (2024)

## Built on by (most cited first, 4 of 4)

- [Ethical and Technical Limits of Deepfake Speech Datasets](https://mlsanity.com/paper/2606.10911/) (2026)
- [RAT: Reference-Augmented Training for ASV Anti-Spoofing](https://mlsanity.com/paper/2606.10908/) (2026)
- [What Do Deepfake Speech Detectors Actually Hear?](https://mlsanity.com/paper/2606.10912/) (2026)
- [An Initial Study of Physiological Markers for Deepfake Speech Detection](https://mlsanity.com/paper/2610.18934/) (2026)

## Abstract (arXiv)

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 logistic regression baselines. The real-valued variant achieves 2.37% EER (0.0684 minDCF) and identifies configurations that match state-of-the-art performance while significantly reducing system complexity, requiring only half the parameters. Our method also provides a diverse set of trade-off solutions, enabling deployment choices that balance accuracy and computational cost.