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SpAArSIST offers a practical optimization for self-supervised anti-spoofing systems by replacing learned graph pooling with lightweight, explicit operations. This approach reduces backend compute by 20.7% and model size by 4.1% while simultaneously improving out-of-domain robustness and lowering error rates. The inclusion of a composite selection score provides a balanced metric for deployment decisions, addressing the critical need for efficiency without sacrificing accuracy.
SpAArSIST: Sparsified AASIST for Efficient and Reliable Anti-Spoofing
arXiv:2606.11674v1 Announce Type: cross Abstract: We present SpAArSIST, a deployment-oriented refinement of the widely used AASIST graph pooling backend for self-supervised learning (SSL) based anti-spoofing. Motivated by redundant operations in public implementations, we replace learned pooling and stack-node attention with explicit, lightweight choices: separate train and inference graph pooling ratios $(k_{\mathrm{tr}},k_{\mathrm{inf}})$, magnitude-based node scoring, and mean aggregation of graph nodes. The best overall configuration (rank 1) cuts backend compute by 20.7% (195.045M $\rightarrow$ 154.706M MACs) and model size by 4.1% (611.8k $\rightarrow$ 586.4k params), while improving out-of-domain robustness on In-the-Wild to 2.82% EER and 0.078 minDCF (from 4.64% and 0.133) and remaining competitive on ASVspoof5. We further provide a composite selection score that summarizes accuracy, calibration, and compute to support balanced deployment-oriented model choice.
Abstract:We present SpAArSIST, a deployment-oriented refinement of the widely used AASIST graph pooling backend for self-supervised learning (SSL) based anti-spoofing. Motivated by redundant operations in public implementations, we replace learned pooling and stack-node attention with explicit, lightweight choices: separate train and inference graph pooling ratios $(k_{\mathrm{tr}},k_{\mathrm{inf}})$, magnitude-based node scoring, and mean aggregation of graph nodes. The best overall configuration (rank 1) cuts backend compute by 20.7% (195.045M $\rightarrow$ 154.706M MACs) and model size by 4.1% (611.8k $\rightarrow$ 586.4k params), while improving out-of-domain robustness on In-the-Wild to 2.82% EER and 0.078 minDCF (from 4.64% and 0.133) and remaining competitive on ASVspoof5. We further provide a composite selection score that summarizes accuracy, calibration, and compute to support balanced deployment-oriented model choice.
Sound (cs.SD); Machine Learning (cs.LG)
I.5.4; I.2.6; H.5.5; D.4.6
arXiv:2606.11674 [cs.SD]
(or arXiv:2606.11674v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2606.11674 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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