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.
The pruned nodes (in red) represent entire regions of space that the algorithm never examines. The points inside those regions are never checked. Compare the "Nodes Visited" count to the total number of points. The quadtree is doing far less work than a brute-force scan.
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