Signature Forgery Detection: Improving Cross-Dataset Generalization

TL;DR

This paper investigates feature learning strategies to improve the cross-dataset generalization of signature forgery detection models, highlighting the potential of raw-image approaches and shell preprocessing for robust biometric verification.

Contribution

It introduces two experimental pipelines for signature forgery detection and analyzes their effectiveness across multiple datasets, aiming to enhance cross-domain robustness.

Findings

Raw-image models outperform shell-based models across benchmarks.

Shell preprocessing shows potential for future improvements.

No clear superiority between the two approaches was established.

Abstract

Automated signature verification is a critical biometric technique used in banking, identity authentication, and legal documentation. Despite the notable progress achieved by deep learning methods, most approaches in offline signature verification still struggle to generalize across datasets, as variations in handwriting styles and acquisition protocols often degrade performance. This study investigates feature learning strategies for signature forgery detection, focusing on improving cross-dataset generalization -- that is, model robustness when trained on one dataset and tested on another. Using three public benchmarks -- CEDAR, ICDAR, and GPDS Synthetic -- two experimental pipelines were developed: one based on raw signature images and another employing a preprocessing method referred to as shell preprocessing. Several behavioral patterns were identified and analyzed; however, no definitive superiority between the two approaches was established. The results show that the raw-image model achieved higher performance across benchmarks, while the shell-based model demonstrated promising potential for future refinement toward robust, cross-domain signature verification.