Handwriting Detectives Using Wavelet Siamese Technology to Verify Signature Fraud

This paper addresses the escalating challenge of signature forgery detection through an innovative hybrid verification system. We integrate Siamese Neural Networks with wavelet scattering transformations to precisely capture signature characteristics while accommodating inherent variations. Our principal contribution, the "common anchor methodology," identifies a singular representative signature per individual, substantially reducing computational demands on the CEDAR Dataset while maintaining verification integrity. Through meticulous optimization of wavelet scattering parameters, our system demonstrates markedly superior performance on the CEDAR benchmark while requiring considerably fewer model parameters than traditional CNN architectures. This research establishes noteworthy advancements in both accuracy and efficiency for practical signature verification implementations. The study evaluates the performance of a wavelet-Siamese network architecture for offline signature verification through a series of five experiments with varying parameter configurations. Key variables include the use of a common anchor, the J Factor, and the θ value. Results reveal that incorporating a common anchor consistently improves performance. Among all configurations, experiment 4 with a J Factor of 2 and a θ value of 16 yielded the most favorable results, achieving the lowest error rate of 20.823% and the highest ROC-AUC score of 0.8699, along with efficient convergence within 55 iterations. In contrast, the absence of a common anchor in Experiment 1 led to a notably higher error rate of 24.44% and lower model performance. These findings demonstrate the critical role of parameter tuning in enhancing the robustness and accuracy of signature verification systems based on Siamese networks. Despite the substantial computational savings, the system’s best achieved error rate (20.82%) remains higher than several state-of-the-art and commercial signature verification solutions, many of which report error rates below 10%. This indicates an existing trade-off between efficiency and the highest attainable accuracy, which future work will aim to mitigate.