GlyphNet’s own results support this: their best CNN (VGG16 fine-tuned on rendered glyphs) achieved 63-67% accuracy on domain-level binary classification. Learned features do not dramatically outperform structural similarity for glyph comparison, and they introduce model versioning concerns and training corpus dependencies. For a dataset intended to feed into security policy, determinism and auditability matter more than marginal accuracy gains.
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，详情可参考搜狗输入法2026
（应受访者要求，刘成、兰丽为化名），这一点在夫子中也有详细论述
machines so that it can be deposited into other machines.