# Reliable Audio Deepfake Detection in Variable Conditions via Quantum-Kernel SVMs

arXiv: https://arxiv.org/abs/2512.18797 · HTML: https://mlsanity.com/paper/2512.18797/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 21 Dec 2025.

## Builds on (most cited first, 12 of 12)

- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [ADD 2023: the Second Audio Deepfake Detection Challenge](https://mlsanity.com/paper/2305.13774/) (2023)
- [Audio Deepfake Detection: A Survey](https://mlsanity.com/paper/2308.14970/) (2023)
- [ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan](https://mlsanity.com/paper/2109.00535/) (2021)
- [Defense Against Adversarial Attacks on Audio DeepFake Detection](https://mlsanity.com/paper/2212.14597/) (2022)
- [Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection](https://mlsanity.com/paper/2410.09250/) (2024)
- [How Deep Are the Fakes? Focusing on Audio Deepfake: A Survey](https://mlsanity.com/paper/2111.14203/) (2021)
- [Deepfake Audio Detection Using Spectrogram-based Feature and Ensemble of Deep Learning Models](https://mlsanity.com/paper/2407.01777/) (2024)
- [Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis](https://mlsanity.com/paper/2507.15636/) (2025)
- [Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact](https://mlsanity.com/paper/2409.10803/) (2024)

## Built on by (most cited first, 3 of 3)

- [Quantum Kernels for Audio Deepfake Detection Using Spectrogram Patch Features](https://mlsanity.com/paper/2605.06035/) (2026)
- [On Evaluating Quantum Kernel Robustness for Low-Resource Cross-Corpus Audio Deepfake Detection](https://mlsanity.com/paper/2610.00649/) (2026)
- [QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing](https://mlsanity.com/paper/2610.16390/) (2026)

## Abstract (arXiv)

Detecting synthetic speech is challenging when labeled data are scarce and recording conditions vary. Existing end-to-end deep models often overfit or fail to generalize, and while kernel methods can remain competitive, their performance heavily depends on the chosen kernel. Here, we show that using a quantum kernel in audio deepfake detection reduces falsepositive rates without increasing model size. Quantum feature maps embed data into high-dimensional Hilbert spaces, enabling the use of expressive similarity measures and compact classifiers. Building on this motivation, we compare quantum-kernel SVMs (QSVMs) with classical SVMs using identical mel-spectrogram preprocessing and stratified 5-fold cross-validation across four corpora (ASVspoof 2019 LA, ASVspoof 5 (2024), ADD23, and an In-the-Wild set). QSVMs achieve consistently lower equalerror rates (EER): 0.183 vs. 0.299 on ASVspoof 5 (2024), 0.081 vs. 0.188 on ADD23, 0.346 vs. 0.399 on ASVspoof 2019, and 0.355 vs. 0.413 In-the-Wild. At the EER operating point (where FPR equals FNR), these correspond to absolute false-positiverate reductions of 0.116 (38.8%), 0.107 (56.9%), 0.053 (13.3%), and 0.058 (14.0%), respectively. We also report how consistent the results are across cross-validation folds and margin-based measures of class separation, using identical settings for both models. The only modification is the kernel; the features and SVM remain unchanged, no additional trainable parameters are introduced, and the quantum kernel is computed on a conventional computer.