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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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The “Knocking on Wood” Wikipedia page mentions Roud’s conclusion, and this claim of a 19th century origin has circulated online as the final say on the matter. If you go back to the original, though, Roud’s proposal actually ends on an ambivalent note: “Before this theory [of 19th century origins] can be finally accepted, however, an examination of the history of European forms of this custom would be advisable.”