# Quantum Kernels for Audio Deepfake Detection Using Spectrogram Patch Features

arXiv: https://arxiv.org/abs/2605.06035 · HTML: https://mlsanity.com/paper/2605.06035/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 13 of 13)

- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [ADD 2022: the First Audio Deep Synthesis Detection Challenge](https://mlsanity.com/paper/2202.08433/) (2022)
- [Quantum Kernel Methods under Scrutiny: A Benchmarking Study](https://mlsanity.com/paper/2409.04406/) (2024)
- [Enhancing Quantum Support Vector Machines through Variational Kernel Training](https://mlsanity.com/paper/2305.06063/) (2023)
- [A Hyperparameter Study for Quantum Kernel Methods](https://mlsanity.com/paper/2310.11891/) (2023)
- [A Comprehensive Survey with Critical Analysis for Deepfake Speech Detection](https://mlsanity.com/paper/2409.15180/) (2024)
- [How Deep Are the Fakes? Focusing on Audio Deepfake: A Survey](https://mlsanity.com/paper/2111.14203/) (2021)
- [Quantum Kernel for Image Classification of Real World Manufacturing Defects](https://mlsanity.com/paper/2212.08693/) (2022)
- [Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis](https://mlsanity.com/paper/2507.15636/) (2025)
- [Reliable Audio Deepfake Detection in Variable Conditions via Quantum-Kernel SVMs](https://mlsanity.com/paper/2512.18797/) (2025)
- [Beyond Identity: A Generalizable Approach for Deepfake Audio Detection](https://mlsanity.com/paper/2505.06766/) (2025)
- [Perturbed Public Voices (P2V): A Dataset for Robust Audio Deepfake Detection](https://mlsanity.com/paper/2508.10949/) (2025)
- [Quantum Approaches for Dysphonia Assessment in Small Speech Datasets](https://mlsanity.com/paper/2502.08968/) (2025)

## Built on by (most cited first, 1 of 1)

- [QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing](https://mlsanity.com/paper/2610.16390/) (2026)

## Abstract (arXiv)

Quantum machine learning has emerged as a promising tool for pattern recognition, yet many audio-focused approaches still treat spectrograms as generic images and do not explicitly exploit their time-frequency structure. We propose Q-Patch, a quantum feature map tailored to audio that encodes local time-frequency patches from mel-spectrograms into quantum states using shallow, hardware-efficient circuits with adjacency-aware entanglement. Each selected patch is summarized by a compact four-dimensional acoustic descriptor and mapped to a four-qubit circuit with depth at most three, enabling practical quantum kernel construction under near-term constraints. We evaluate Q-Patch on an audio spoofing detection task using a controlled, balanced protocol and compare it with size-matched classical baselines. Q-Patch improves discrimination between bona fide and spoofed samples, achieving an area under the receiver operating characteristic curve (AUROC) of 0.87, compared with 0.82 for a radial basis function support vector machine (RBF-SVM) trained on the same patch-level features. Kernel-space analysis further reveals a clear class structure, with cross-class similarity around 0.615 and within-class self-similarity of 1.00. Overall, Q-Patch provides a practical framework for incorporating time-frequency-aware representations into quantum kernel learning for audio authenticity assessment in low-resource settings.