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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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另一方面,硬件集成的防窥屏幕也是在 2026 电子产品大涨价、硬件配置边际效应递减的大背景下,维持持续吸引力的关键卖点。,这一点在夫子中也有详细论述

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