SinoGlyphBench: A Diagnostic Benchmark for Chinese Glyph-Level Obfuscation in Language-Model Moderation
Year: 2026
Type: conference-paper
Abstract: Glyph-level obfuscation can leave harmful Chinese content readable to humans while degrading automated moderation.We introduce SINO-GLYPHBENCH, a diagnostic benchmark that identifies label-critical semantic anchors and creates matched original and glyph-obfuscated inputs in text and image modalities.By perturbing anchors, background context, or both, this design distinguishes corruption of moderationrelevant evidence from general surface variation.Across 176,916 paired evaluations of 12 LLMs and MLLMs, obfuscation increases harmful false-negative and false-positive rates by 6.1 and 4.7 percentage points, respectively, and reduces four-way accuracy by 5.0 points.Models retain 75.7% of the decisions that were correct on the matched original inputs.Fullscope perturbations cause the largest degradation, anchor-only perturbations are more damaging than background-only perturbations, and cross-script substitution is particularly difficult in the text modality.Analysis of structured outputs identifies observable mismatches in visible-form reading, intended-message recovery, and final safety judgment.The evaluated models, therefore, remain brittle to Chinese content written with non-canonical glyphs.Resources are available at https://github. com/fengshun124/SinoGlyphBench.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7212071908