FoeGlass is a black-box red-teaming method that leverages LLM in-context learning with diversity-based prompting to generate adversarial audio samples, raising false negative rates of ADD models by up to 94% over baselines.
Measuring the ro- bustness of audio deepfake detectors
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 2polarities
background 2representative citing papers
Alethia is a pretrained audio encoder using continuous embedding prediction and generative flow-matching reconstruction that outperforms existing speech foundation models on voice deepfake tasks with better robustness and zero-shot generalization.
Proteus automates discovery of audio transformation chains that evade deepfake detectors using BFS and Q-learning, then uses the results to retrain the detector.
RADAR Challenge 2026 organizes a multilingual audio deepfake detection benchmark with media transformations, reporting participation from 33 development and 22 evaluation teams using EER metric.
citing papers explorer
-
FoeGlass: Simple In-Context Learning Is Enough for Red Teaming Audio Deepfake Detectors
FoeGlass is a black-box red-teaming method that leverages LLM in-context learning with diversity-based prompting to generate adversarial audio samples, raising false negative rates of ADD models by up to 94% over baselines.
-
Alethia: A Foundational Encoder for Voice Deepfakes
Alethia is a pretrained audio encoder using continuous embedding prediction and generative flow-matching reconstruction that outperforms existing speech foundation models on voice deepfake tasks with better robustness and zero-shot generalization.
-
Proteus: Automated Adversarial Robustness Testing for Audio Deepfake Detectors
Proteus automates discovery of audio transformation chains that evade deepfake detectors using BFS and Q-learning, then uses the results to retrain the detector.
-
RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations
RADAR Challenge 2026 organizes a multilingual audio deepfake detection benchmark with media transformations, reporting participation from 33 development and 22 evaluation teams using EER metric.