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.
"These types of surgeries are too risky, yet it's still so accessible," she said.
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Finally, really small actuators made using traditional electric motors tend to get too hot for their own good at such scales – another headache.