ReCAST: Restoration-aware Cascaded Stage-wise Training for Obfuscated SMS Risk Classification

2026-09-04

AI Summary

针对中文短信中欺诈信息的混淆问题，提出ReCAST框架，通过级联阶段训练提升小型模型的去混淆能力，从而提高风险分类性能。

Abstract

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.

Problem

• obfuscated SMS
• risk classification
• Chinese SMS

Innovation

• Restoration-aware
• Cascaded Stage-wise Training
• De-obfuscation