← Back to all news
arXiv AI By Jieyun Huang, Yi Shen, Kaikai Zhao, Jiangze Yan, Wenjing Zhang, Ping Chen, Ning Wang, Zhaoxiang Liu, Kai Wang, Shiguo Lian

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

Read the original on arXiv AI →

ReCAST is a framework that trains a small student model to classify obfuscated Chinese SMS messages by first learning to detect obfuscated spans, predict obfuscation types, and restore the original text using knowledge distilled from a large teacher model. The student model then performs risk classification, achieving better performance than directly trained baselines on a real-world Chinese SMS benchmark. This approach demonstrates that restoration-aware distillation can produce robust, production‑ready SMS risk classifiers with reduced model size.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Computation and Language
Sep 28

Prompt Injection Detection for Email Agents Through Attack Chain Modeling

The paper introduces a prompt‑injection detection framework for email assistants that models attacks as a chain of stages. It combines a text detector, stage‑specific verifiers, rule‑based risk signals, user intent consistency checks, and a logistic decision policy. Experiments on five benchmarks show the framework outperforms pretrained detectors, achieving a mean F1 of 0.406 versus 0.216, and demonstrate that training on benign emails resembling attacks reduces false alarms.

By Ahmad Hashmi, Dhyey Patel, Yunting Yin
llmsagentsnlpbenchmarks
More like this →
arXiv Machine Learning
Oct 3

Evasion Attacks: How Adversarial Noise Bypasses ML Classifiers

The paper reports a reproducible study of evasion attacks on image and text classifiers. A compact convolutional network on MNIST achieved 98.63% clean accuracy but dropped to 60.20% under FGSM with ε=0.15 and 1.72% with ε=0.30, while PGD reduced accuracy to 32.47% and 0.41%; a bit‑depth‑reduction defense only partially restored performance. In contrast, a DistilBERT model fine‑tuned on the SMS Spam Collection reached 98.75% accuracy and 94.96% F1‑score, yet a sequence of predefined perturbations produced only modest probability shifts and did not flip spam to ham predictions.

By Parker Hummel (Minot State University), Ryne Skabo (Minot State University), Muhammad Abusaqer (Minot State University)
computer-visionnlpfine-tuningsafety
More like this →