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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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The bulk of this is thought to have been accrued by five key departments – the Cabinet Office, Home Office, Department of Health and Social Care, Treasury and UK Health Security Agency, which have been repeatedly asked to provide evidence.
精细化管控:支持分批、分级、按需迁移