用可视化技术揭示神经网络在笔迹识别中的判断依据。
Transparency Techniques for Neural Networks trained on Writer Identification and Writer Verification
- 引入像素级与点对点显著性图，分析模型关注区域。
- 像素级方法在评分中表现更优，能有效支持法医分析。
- 帮助理解模型决策过程，提升笔迹鉴定可信度。
神经网络是计算机视觉领域中笔迹识别（WI）和笔迹验证（WV）的主流方法，但其'黑箱'特性影响了性能与可靠性。本文首次在该领域应用两种可解释性技术：像素级显著性图与点对点显著性图，分别用于展示图像关键区域及两幅手写文本间的相似性。通过删除与插入评分指标评估效果，结果表明像素级显著性图优于点对点方法，且其突出区域与法医专家判断区域高度一致，有助于为法医提供判别依据并揭示模型选择的关键特征。
原文摘要 · Abstract (English)
Neural Networks are the state of the art for many tasks in the computer vision domain, including Writer Identification (WI) and Writer Verification (WV). The transparency of these "black box" systems is important for improvements of performance and reliability. For this work, two transparency techniques are applied to neural networks trained on WI and WV for the first time in this domain. The first technique provides pixel-level saliency maps, while the point-specific saliency maps of the second technique provide information on similarities between two images. The transparency techniques are evaluated using deletion and insertion score metrics. The goal is to support forensic experts with information on similarities in handwritten text and to explore the characteristics selected by a neural network for the identification process. For the qualitative evaluation, the highlights of the maps are compared to the areas forensic experts consider during the identification process. The evaluation results show that the pixel-wise saliency maps outperform the point-specific saliency maps and are suitable for the support of forensic experts.
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