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.
"I wouldn’t be the first to point out that a lot of this is down to the influence of social media and the way in which it has given vent to the darkest parts of the human soul. Not just given vent to them, but actively amplified them and pushed them into our feeds. So yeah, this is not a niche subject."
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