SpAArSIST: Sparsified AASIST for Efficient and Reliable Anti-Spoofing
For researchers deploying anti-spoofing systems, this work offers a more efficient and robust backend with minimal accuracy loss.
SpAArSIST refines the AASIST backend for SSL-based anti-spoofing by replacing learned pooling with explicit lightweight choices, reducing compute by 20.7% and model size by 4.1%, while improving out-of-domain robustness (EER from 4.64% to 2.82%).
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.