@inproceedings{huang-etal-2026-recast,
title = "{R}e{CAST}: Restoration-aware Cascaded Stage-wise Training for Obfuscated {SMS} Risk Classification",
author = "Huang, Jieyun and
Shen, Yi and
Zhao, Kaikai and
Yan, Jiangze and
Zhang, Wenjing and
Chen, Ping and
Wang, Ning and
Liu, Zhaoxiang and
Wang, Kai and
Lian, Shiguo",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.234/",
pages = "3915--3931",
ISBN = "979-8-89176-460-6",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T ReCAST: Restoration-aware Cascaded Stage-wise Training for Obfuscated SMS Risk Classification
%A Huang, Jieyun
%A Shen, Yi
%A Zhao, Kaikai
%A Yan, Jiangze
%A Zhang, Wenjing
%A Chen, Ping
%A Wang, Ning
%A Liu, Zhaoxiang
%A Wang, Kai
%A Lian, Shiguo
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F huang-etal-2026-recast
%X 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.
%U https://aclanthology.org/2026.emnlp-industry.234/
%P 3915-3931
Markdown (Informal)
[ReCAST: Restoration-aware Cascaded Stage-wise Training for Obfuscated SMS Risk Classification](https://aclanthology.org/2026.emnlp-industry.234/) (Huang et al., EMNLP 2026)
ACL
- Jieyun Huang, Yi Shen, Kaikai Zhao, Jiangze Yan, Wenjing Zhang, Ping Chen, Ning Wang, Zhaoxiang Liu, Kai Wang, and Shiguo Lian. 2026. ReCAST: Restoration-aware Cascaded Stage-wise Training for Obfuscated SMS Risk Classification. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 3915–3931, Budapest, Hungary. Association for Computational Linguistics.