LID separates adversarial from benign speech perturbations, transcript-free
measured in 1 paperArcos-Holzinger et al. compute per-layer Local Intrinsic Dimensionality (framework GRIDS) on WavLM-base and wav2vec 2.0 base representations under acoustic perturbation [arcosholzinger-etal-2026-dimensionality-aware-anomaly-detection-in-ssl-speech-models] LID rises for all low-SNR perturbations, but as SNR increases benign noise's LID converges back toward the clean profile while adversarial inputs retain elevated early-layer LID [arcosholzinger-etal-2026-dimensionality-aware-anomaly-detection-in-ssl-speech-models] This per-layer LID divergence distinguishes adversarial from benign perturbations at AUROC 0.78-1.00 without ground-truth transcripts [arcosholzinger-etal-2026-dimensionality-aware-anomaly-detection-in-ssl-speech-models]
Context
speech self-supervised learning, local intrinsic dimensionality, adversarial robustness, anomaly detection, layer-wise geometry
Confirmed in models
Papers
Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models — Arcos-Holzinger, Sandra, Erfani, Sarah M., Bailey, James, Khudanpur, Sanjeev