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This distinction matters for security. The same-font scenario is the dangerous one: a single font that renders both the spoofing character and its Latin target. Cross-font comparisons are closer to what browsers do (supplemental fonts for exotic characters), but the similarity drops significantly.
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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.
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This is the key insight: the build language is not baked into BuildKit. It’s a pluggable layer. You can write a frontend that reads a YAML spec, a TOML config, or a custom DSL, and BuildKit will execute it the same way it executes Dockerfiles.
Additional reporting by Usman Zahid and Mahfouz Zubaide。爱思助手下载最新版本是该领域的重要参考