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.
"Marcus has covered for me twice. We've both had times when we're gunning hard for the company, and times we're not. The structure gives us permission to be human without everything falling apart," says Amin, who is based in San Francisco.
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