Introduces the LDD task, ListenForge dataset built from five listening head generation methods, and MANet model that detects listening forgeries via motion inconsistencies guided by audio semantics.
Asvspoof2021:Automaticspeakerverificationspoofingandcountermeasureschallengeevaluationplan
4 Pith papers cite this work. Polarity classification is still indexing.
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Phoneme-level analysis using self-supervised embeddings identifies higher divergence in complex vowels and fricatives for emotional voice conversion deepfakes, enabling more interpretable detection across emotions.
A dual-branch fusion model with XLS-R, BEATs, Matching Head, and cross-attention achieves 70.20% F1-score and 16.54% environmental EER on CompSpoofV2, outperforming the baseline for component-level deepfake detection.
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.
citing papers explorer
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Listening Deepfake Detection: A New Perspective Beyond Speaking-Centric Forgery Analysis
Introduces the LDD task, ListenForge dataset built from five listening head generation methods, and MANet model that detects listening forgeries via motion inconsistencies guided by audio semantics.
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Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings
Phoneme-level analysis using self-supervised embeddings identifies higher divergence in complex vowels and fricatives for emotional voice conversion deepfakes, enabling more interpretable detection across emotions.
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Deepfake Audio Detection Using Self-supervised Fusion Representations
A dual-branch fusion model with XLS-R, BEATs, Matching Head, and cross-attention achieves 70.20% F1-score and 16.54% environmental EER on CompSpoofV2, outperforming the baseline for component-level deepfake detection.
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From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.