13 papers
An Initial Study of Physiological Markers for Deepfake Speech Detection
Vojtěch Staněk, Michal Frič, Kamil Malinka +1
Conventional deepfake speech detectors primarily target synthesis artifacts and often degrade under unseen attacks or recording conditions. Human speech, however, contains physiolo…
Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors
Lukáš Prokeš, Vojtěch Staněk, Anton Firc +1
Deepfake speech detectors are typically evaluated as binary classifiers on fixed benchmarks, but deployed systems face evolving attacks, codecs, speakers, and recording conditions,…
Not All Challenges Are Equal: Evaluating Interactive Liveness Detection Against Real-Time Face Swaps
Anton Firc, David Drtil, Martin Perešíni +3
Remote identity verification assumes that a camera stream shows a live person, yet public real-time face-swap tools can replace that person before the stream reaches the verifier.…
QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing
Anton Firc, Nouhaila Innan, Vojtěch Staněk +6
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…
Tracking the Trend in How Speech Synthesizers Deceive People
Milan Šalko, Anton Firc, Kamil Malinka +4
Advances in speech synthesis have made deepfake audio highly realistic. Earlier studies reported 70-80% human detection accuracy, but relied primarily on older synthesizers. We com…
The Last Mile of Deepfake Speech Detection: An Industry-Academia Experience Report
Anton Firc, Kamil Malinka, Vojtěch Staněk +2
Synthetic speech detection benchmarks now report sub-1% error rates on some in-domain evaluations, yet performance degrades under unseen attacks, channel mismatch, and distribution…
SpAArSIST: Sparsified AASIST for Efficient and Reliable Anti-Spoofing
Anton Firc, Vojtěch Staněk, Zbyněk Lička +2
We present SpAArSIST, a deployment-oriented refinement of the widely used AASIST graph pooling backend for self-supervised learning (SSL) based anti-spoofing. Motivated by redundan…
The Hidden Cost of Pairwise Verification in Synthetic Speech Source Tracing
Anton Firc, Zbyněk Lička, Vojtěch Staněk +1
Open-set source tracing is increasingly framed as a verification problem, motivating the use of pairwise metric-learning objectives from biometrics. We thus compare global anchorin…
What Do Deepfake Speech Detectors Actually Hear?
Vojtěch Staněk, Veronika Jirmusová, Anton Firc +3
Deepfake speech detectors often output a single score without explaining why an audio sample is flagged, where in the signal the evidence lies, or what cues drive the decision. We…
Ethical and Technical Limits of Deepfake Speech Datasets
Vojtěch Staněk, Eva Trnovská, Kamil Malinka +1
Claims about the robustness and fairness of deepfake speech detectors are only as credible as the datasets used to train and evaluate those systems. We present a dataset-level audi…
RAT: Reference-Augmented Training for ASV Anti-Spoofing
Vojtěch Staněk, Anton Firc, Jakub Reš +1
We introduce a spoofing countermeasure architecture conditioned on speaker-reference recordings, but observe that it converges to a solution that effectively ignores the reference…
Evolutionary Multi-Objective Fusion of Deepfake Speech Detectors
Vojtěch Staněk, Martin Perešíni, Lukáš Sekanina +2
While deepfake speech detectors built on large self-supervised learning (SSL) models achieve high accuracy, employing standard ensemble fusion to further enhance robustness often r…
SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis
Vojtěch Staněk, Karel Srna, Anton Firc +1
Despite growing attention to deepfake speech detection, the aspects of bias and fairness remain underexplored in the speech domain. To address this gap, we introduce the Speaker Ch…