ProtoSig: Enhancing training data for offline handwritten signature verification using prototypical signatures

Offline Handwritten Signature Verification (offline HSV) analyzes static images of signatures to distinguish between genuine and forged samples. To create training data for such systems, negative samples, commonly known as random forgeries, are typically drawn from genuine signatures of other users, as real-world datasets often lack actual forgeries. While this strategy helps address the scarcity of forgery data, it faces several challenges. The randomly selected samples may lack the diversity and challenge needed to improve model robustness. Additionally, they can cause redundancy, increasing training time and storage requirements, and may introduce bias across users, leading to unfair training distributions. This paper proposes a novel strategy, called ProtoSig, for generating more informative and diverse negative samples by leveraging prototypical signatures, which are compact, non-identifiable vectors obtained through a data-driven summarization of signature feature vectors. Our experiments demonstrate that ProtoSig enhances skilled forgery detection in a writer-dependent verification approach, eliminating performance variability across runs, while reducing dependence on external user data. We further demonstrate that our method achieves comparable or higher accuracy with a smaller training set, yielding substantial scalability benefits: over 98% reduction in training time and up to two orders of magnitude lower computational cost (in FLOPs), while strengthening data privacy and promoting fairness in offline HSV systems.