发表机构
Unicom Data Intelligence, China Unicom; Data Science & Artificial Intelligence Research Institute, China Unicom（中国联通数据智能公司; 中国联通数据科学与人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对混淆短信欺诈问题，提出ReCAST框架，通过知识蒸馏将大模型去混淆能力迁移至小模型，提升了混淆场景下的短信风险分类性能。
AI 中文摘要
通过短消息服务（SMS）发送的欺诈消息正日益被混淆，以规避生产系统中注重成本的分类器。在中国短信中，攻击者可利用大量精心设计的混淆策略隐藏风险短语，同时保持人类可读性，使得在实际延迟和吞吐量约束下的直接分类变得脆弱。我们提出ReCAST，即面向稳健混淆中国短信分类的感知恢复级联分阶段训练框架。ReCAST通过监督混淆跨度检测、混淆类型预测和文本恢复，将大型教师模型的去混淆能力提炼为更小的可部署学生模型，随后利用感知恢复的学生模型进行下游风险分类。在内部构建的真实中国短信基准上的实验表明，ReCAST在混淆情况下相比直接训练的基线显著提升了分类性能。结果表明，感知恢复型知识蒸馏为在面向生产的约束下使用更小的可部署模型实现稳健的短信风险分类提供了可行路径。
英文摘要
Fraudulent messages sent via Short Message Service (SMS) are increasingly obfuscated to evade cost-conscious classifiers in production systems. In Chinese SMS, attackers can exploit a wide range of carefully crafted obfuscation strategies to hide risk-bearing phrases while preserving human readability, making direct classification brittle under real-world latency and throughput constraints. We propose ReCAST, a Restoration-aware Cascaded Stage-wise Training framework for robust obfuscated Chinese SMS classification. ReCAST distills a large teacher model's de-obfuscation ability into a smaller deployable student model by supervising obfuscated span detection, obfuscation type prediction, and text restoration, and then uses the restoration-aware student for downstream risk classification. Experiments on an internally constructed real-world Chinese SMS benchmark show that ReCAST substantially improves classification performance over directly trained baselines under obfuscation. The results suggest that restoration-aware distillation offers a practical path toward robust SMS risk classification with smaller deployable models under production-oriented constraints.
CommentsAccept by EMNLP 2026 Industry Track
