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.

翻译：我们提出了SpAArSIST，这是对基于自监督学习（SSL）的反欺骗任务中广泛使用的AASIST图池化后端进行部署导向优化的改进方案。鉴于公开实现中存在冗余操作，我们将学习型池化与栈节点注意力替换为显式轻量化选择：分离训练与推理图池化比率$(k_{\mathrm{tr}},k_{\mathrm{inf}})$、基于幅度的节点评分机制以及图节点的均值聚合策略。最优整体配置（排名第一）将后端计算量削减20.7%（195.045M MACs→154.706M MACs），模型规模缩减4.1%（611.8k参数→586.4k参数），同时在In-the-Wild数据集上将域外鲁棒性提升至2.82%等错误率（EER）和0.078最小检测代价函数（minDCF）（原为4.64%和0.133），并在ASVspoof5数据集上保持竞争力。我们进一步提供综合选择评分，该评分通过汇总精度、校准度与计算开销三个指标，为平衡部署导向的模型选择提供支持。