Evolutionary Multi-Objective Fusion of Deepfake Speech Detectors
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel multi-objective evolutionary framework for fusing deepfake speech detectors, achieving state-of-the-art performance while significantly reducing system complexity. This work is a substantial contribution to the field of audio machine learning, providing a comprehensive approach to tackle the challenges posed by deepfake technologies.
Methodology
The paper introduces an innovative multi-objective evolutionary framework for fusing deepfake speech detectors using NSGA-II, addressing the critical balance between detection accuracy and system complexity. It explores two encoding strategies—binary-coded detector selection and real-valued weight optimization—demonstrating a systematic approach to ensemble learning that is both effective and efficient. The methodology is well-structured, leveraging evolutionary algorithms to navigate the trade-offs inherent in deepfake detection.
Results & evaluation
The authors conduct extensive experiments on the ASVspoof 5 dataset, utilizing a diverse pool of 36 SSL-based detectors. The results are robust, showcasing the superiority of the proposed methods over traditional fusion techniques, including simple averaging and logistic regression. The achieved EER of 2.37% indicates a significant performance improvement while reducing system complexity, underscoring the effectiveness of the proposed approach.
Reproducibility
The paper provides detailed implementation information, including parameter settings, computational resources, and the use of a GitHub repository for code access. The thoroughness of the experimental setup and the availability of the code enhance the reproducibility of the results, allowing other researchers to validate and build upon this work.
Limitations
While the proposed method is effective, it is limited by its reliance on score-level fusion, which may overlook deeper interactions that could be exploited through joint fine-tuning of the models. Additionally, the performance is constrained by the quality of the underlying detectors, suggesting that optimizing these base models could further enhance the fusion outcomes.
Broader impact
This research has significant implications for the field of deepfake detection, particularly in enhancing the robustness and efficiency of voice biometric systems. The ability to balance performance and complexity in detector fusion can lead to more practical applications in security and authentication, addressing the growing concerns surrounding deepfake technology. The paper presents a novel multi-objective evolutionary framework for fusing deepfake speech detectors, achieving state-of-the-art performance while significantly reducing system complexity. This work is a substantial contribution to the field of audio machine learning, providing a comprehensive approach to tackle the challenges posed by deepfake technologies.
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