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.
Review (Ling + Tbox): 利用 Tbox 的评论功能，让 Ling 扮演不同角色（测试、产品、用户）进行多视角验收。。同城约会是该领域的重要参考
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