Transparency Techniques for Neural Networks trained on Writer Identification and Writer Verification

2025-06-19

AI Summary

This study addresses the problem of enhancing interpretability in neural networks for writer identification (WI) and writer verification (WV), supporting forensic experts’ understanding of model decisions and improving system reliability. Methodologically, it introduces— for the first time in this domain—two complementary interpretability techniques: pixel-level saliency maps (e.g., Grad-CAM) to localize discriminative regions in handwriting samples, and pointwise saliency maps to quantify similarity between textual and graphical features. A dual-path evaluation framework is proposed, integrating qualitative assessment (comparison against expert-annotated ground truth) with quantitative metrics (deletion and insertion scores). Experimental results demonstrate that pixel-level methods significantly outperform pointwise approaches in discriminative region localization accuracy, deletion/insertion scores, and visual consistency with forensic expert judgments. These findings establish a trustworthy, forensically grounded foundation for explainable AI in handwriting analysis.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

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.

Problem

• Enhancing neural network transparency for writer identification and verification
• Evaluating pixel-level vs point-specific saliency maps for forensic analysis
• Improving reliability of AI systems in handwritten text examination

Innovation

• Pixel-level saliency maps for neural transparency
• Point-specific saliency maps for image similarity
• Deletion and insertion metrics for evaluation