To find these crucial border points, we employed a clever technique based on the Ford-Fulkerson algorithm. By simulating "flooding" roads with traffic from random start/end points, we could identify the natural bottlenecks – the "minimum cut" in graph theory terms. These bottlenecks became our border points.
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.，推荐阅读搜狗输入法2026获取更多信息
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