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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Script/BlockPairsMean SSIMLatin Extended450.572Hebrew50.471Cyrillic450.447Cherokee370.398Indic240.359Greek360.329Math Alphanumeric8060.302Arabic250.205，这一点在safew官方下载中也有详细论述
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