发表机构
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究比较量子支持向量机、经典支持向量机与多层感知器在低资源跨语料库音频深度伪造检测中的表现，发现量子核在严重域偏移下保持判别力，但无一致优势。
AI 中文摘要
合成语音检测对于音频安全至关重要，但当标注数据稀缺且评估条件与训练条件不同时，性能可能会下降。本研究考察了在有限训练数据下，量子核方法和轻量级神经网络模型用于跨语料库音频深度伪造检测的表现。我们比较了量子支持向量机（QSVM）、经典支持向量机（SVM）和多层感知器（MLP），所有模型均使用冻结的wav2vec 2.0嵌入，并严格限制在200个训练样本的预算内。为了匹配近期量子硬件的量子比特预算，使用主成分分析将嵌入降至四维，所有模型使用相同的降维特征。在ASVspoof 2019、ASVspoof 5、ADD 2023挑战赛和In-the-Wild数据集上的实验表明，在从ASVspoof 2019到ADD 2023的严重域偏移下，MLP性能退化至接近随机，曲线下面积约为50%，等错误率为50.0%。相比之下，QSVM保持了有意义的判别能力，曲线下面积达到76.0%，等错误率为27.0%。这一优势在跨域方向上并不一致。当在ADD 2023上训练时，QSVM在三个迁移中的两个上低于随机水平，而MLP表现更好。这些结果表明，量子核方法在严重的跨语料库偏移和严格的低资源约束下可以具有竞争力，但在近域迁移下并未提供一致的优势。我们将这些发现解释为量子核在分布偏移下归纳偏置的经验表征，而非量子优势的证据，因为四量子比特核可以在经典硬件上精确模拟。
英文摘要
Synthetic speech detection is critical for audio security, but performance can degrade when labeled data are scarce and evaluation conditions differ from training. This study examines quantum kernel methods and lightweight neural models for cross-corpus audio deepfake detection under limited training data. We compare a Quantum Support Vector Machine (QSVM), a classical support vector machine (SVM), and a multilayer perceptron (MLP), all trained on frozen wav2vec 2.0 embeddings using a strict budget of 200 training samples. To match the qubit budget of near-term quantum hardware, embeddings are reduced to four dimensions using principal component analysis, and all models use the same reduced features. Experiments on ASVspoof 2019, ASVspoof 5, the ADD 2023 Challenge, and the In-the-Wild dataset show that under severe domain shift from ASVspoof 2019 to ADD 2023, the MLP degrades to near-random performance, with an area under the curve of approximately 50% and an equal error rate of 50.0%. In contrast, the QSVM maintains meaningful discrimination, achieving an area under the curve of 76.0% and an equal error rate of 27.0%. This advantage is not consistent across transfer directions. When trained on ADD 2023, the QSVM falls below chance on two of three transfers, while the MLP performs better. These results suggest that quantum kernel methods can be competitive under severe cross-corpus shifts and strict low-resource constraints, but do not provide a consistent advantage under near-domain transfer. We interpret these findings as an empirical characterization of quantum kernel inductive bias under distribution shift, rather than evidence of quantum advantage, since the four-qubit kernel can be simulated exactly on classical hardware.
