A four-backbone SSL ensemble achieves near-perfect deepfake detection in the ImageCLEF 2026 track, while the same team's generated audio ranks first in the generation track, revealing an OR-versus-AND asymmetry between defenders and attackers.
End-to-end anti-spoofing with RawNet2
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abstract
Spoofing countermeasures aim to protect automatic speaker verification systems from attempts to manipulate their reliability with the use of spoofed speech signals. While results from the most recent ASVspoof 2019 evaluation show great potential to detect most forms of attack, some continue to evade detection. This paper reports the first application of RawNet2 to anti-spoofing. RawNet2 ingests raw audio and has potential to learn cues that are not detectable using more traditional countermeasure solutions. We describe modifications made to the original RawNet2 architecture so that it can be applied to anti-spoofing. For A17 attacks, our RawNet2 systems results are the second-best reported, while the fusion of RawNet2 and baseline countermeasures gives the second-best results reported for the full ASVspoof 2019 logical access condition. Our results are reproducible with open source software.
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Multi-Backbone Self-Supervised Ensembles for Audio Deepfake Detection and a Cross-Track Analysis of Generation-Detection Asymmetry
A four-backbone SSL ensemble achieves near-perfect deepfake detection in the ImageCLEF 2026 track, while the same team's generated audio ranks first in the generation track, revealing an OR-versus-AND asymmetry between defenders and attackers.