Quantum circuits improve fairness in deepfake speech detection confidence
QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing
Machine Learning
Summary
Detecting fake or synthetic speech is important to stop misuse, but it needs not just accuracy but trustworthy confidence scores. The authors introduced a small quantum circuit as part of a deep learning model to better judge how sure the system is about its decisions. This quantum part improved the reliability of confidence scores, including when tested on unusual or new types of fake speech. They even tested the quantum part on a real quantum processor, showing it can work outside simulations, with some mixed results on different measures.
What this means in practice
- •For voice security teams: Improve the reliability of confidence scores for detecting synthetic speech to reduce costly overconfident errors in speaker verification systems.
- •For quantum hardware developers: Use a tested small quantum circuit as a hardware bottleneck example that runs on current quantum processors for machine learning applications.
Authors
Anton Firc, Nouhaila Innan, Vojtěch Staněk, Kamil Malinka, Martin Perešini, Zbyněk Lička, Walid El Maouaki, Alberto Marchisio, Muhammad Shafique
Abstract
A central goal of quantum machine learning is to identify applications in which small quantum circuits can improve task performance while remaining executable on real hardware. We investigate deepfake (synthetic) speech detection, a largely unexplored application in this area, where reliable confidence scores are as important as discrimination performance because deployment decisions are made at fixed thresholds and overconfident errors can be costly. We introduce QBFF, which routes a compressed speech representation through a small 8-qubit variational circuit (VQC) used as a drop-in bottleneck, with a matched classical bottleneck as the control. The quantum circuit yields better-calibrated confidence in most matched comparisons, including out-of-distribution evaluation, though the downstream deployment-cost benefit is model-dependent. We then run the same circuit on a real IBM quantum processor. To our knowledge, this is the first anti-spoofing pipeline to run on a QPU; hardware execution demonstrates feasibility, with lower subset EER and $C_{llr}$ but higher mean actDCF than the matched classical reference. Together, these results show that hardware-executable quantum bottlenecks can improve score reliability, with gains that vary by evaluation setting.