用轻量模型预判手机拍图是否适合防伪验证，降低误拒率。
Predictive Quality Assessment for Mobile Secure Graphics
- 通过预测视频帧对验证任务的适用性，避开低质量图像
- 在3.2万张手机拍摄图像上实现92.1%的验证准确率
- 冻结通用模型比全微调更适配不同印刷设备，利于实际部署
智能手机采集安全图形时因环境不可控，导致高误拒率，造成显著‘可靠性缺口’。为此，我们摒弃传统感知图像质量评估，提出一种预测性框架，预先判断视频帧是否适合后续资源密集型验证。设计轻量模型预测画面质量得分，决定是否提交给高成本验证模型。在包含105部手机、超3.2万张图像的大规模数据集上，采用重新定义的FNMR和ISRR指标进行验证。此外，跨域分析发现：在不同工业印刷机生成的图形上，一个冻结的ImageNet预训练网络作为轻量探针，泛化能力优于完全微调的模型。这表明：面对物理制造领域的分布偏移，冻结通用骨干网络比全微调更具鲁棒性，避免对源域特征过拟合。
原文摘要 · Abstract (English)
The reliability of secure graphic verification, a key anti-counterfeiting tool, is undermined by poor image acquisition on smartphones. Uncontrolled user captures of these high-entropy patterns cause high false rejection rates, creating a significant 'reliability gap'. To bridge this gap, we depart from traditional perceptual IQA and introduce a framework that predictively estimates a frame's utility for the downstream verification task. We propose a lightweight model to predict a quality score for a video frame, determining its suitability for a resource-intensive oracle model. Our framework is validated using re-contextualized FNMR and ISRR metrics on a large-scale dataset of 32,000+ images from 105 smartphones. Furthermore, a novel cross-domain analysis on graphics from different industrial printing presses reveals a key finding: a lightweight probe on a frozen, ImageNet-pretrained network generalizes better to an unseen printing technology than a fully fine-tuned model. This provides a key insight for real-world generalization: for domain shifts from physical manufacturing, a frozen general-purpose backbone can be more robust than full fine-tuning, which can overfit to source-domain artifacts.
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