Similarity Choice and Negative Scaling in Supervised Contrastive Learning for Deepfake Audio Detection
Supervised contrastive learning (SupCon) is widely used to shape representations, but has seen limited targeted study for audio deepfake detection. Existing work typically combines contrastive terms with broader pipelines; however, the focus on SupCon itself is missing. In this work, we run a controlled study on wav2vec2 XLS-R (300M) that varies (i) similarity in SupCon (cosine vs angular similarity derived from the hyperspherical angle) and (ii) negative scaling using a warm-started global cross-batch queue. Stage 1 fine-tunes the encoder and projection head with SupCon; Stage 2 freezes them and trains a linear classifier with BCE. Trained on ASVspoof 2019 LA and evaluated on ASV19 eval ...
| Authors | Jaskirat Sudan, Hashim Ali, Surya Subramani et al. |
|---|---|
| Primary category | eess.AS |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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