The surge in counterfeit signatures has inflicted widespread inconveniences and formidable challenges for both individuals and organizations. This groundbreaking research paper introduces SigScatNet, an innovative solution to combat this issue by harnessing the potential of a Siamese deep learning network, bolstered by Scattering wavelets, to detect signature forgery and assess signature similarity. The Siamese Network empowers us to ascertain the authenticity of signatures through a comprehensive similarity index, enabling precise validation and comparison. Remarkably, the integration of Scattering wavelets endows our model with exceptional efficiency, rendering it light enough to operate seamlessly on cost-effective hardware systems. To validate the efficacy of our approach, extensive experimentation was conducted on two open-sourced datasets: the ICDAR SigComp Dutch dataset and the CEDAR dataset. The experimental results demonstrate the practicality and resounding success of our proposed SigScatNet, yielding an unparalleled Equal Error Rate of 3.689% with the ICDAR SigComp Dutch dataset and an astonishing 0.0578% with the CEDAR dataset. Through the implementation of SigScatNet, our research spearheads a new state-of-the-art in signature analysis in terms of EER scores and computational efficiency, offering an advanced and accessible solution for detecting forgery and quantifying signature similarities. By employing cutting-edge Siamese deep learning and Scattering wavelets, we provide a robust framework that paves the way for secure and efficient signature verification systems.

翻译：伪造签名的激增给个人及组织带来了广泛不便与严峻挑战。本开创性研究论文提出SigScatNet，这是一种创新性解决方案，通过利用由散射小波增强的孪生深度学习网络来检测签名伪造并评估签名相似度。孪生网络使我们能够通过综合相似度指标判定签名的真实性，从而实现精准验证与比对。值得注意的是，散射小波的引入赋予模型卓越效率，使其轻量化到可在低成本硬件系统上无缝运行。为验证方法有效性，我们在两个开源数据集（ICDAR SigComp荷兰数据集与CEDAR数据集）上进行了广泛实验。实验结果证明了所提SigScatNet的实用性与显著成功：在ICDAR SigComp荷兰数据集上达到3.689%的等错误率，在CEDAR数据集上更实现惊人的0.0578%等错误率。通过SigScatNet的实施，本研究在等错误率得分与计算效率方面引领了签名分析领域的新标杆，为检测伪造与量化签名相似度提供了先进且易用的解决方案。通过采用前沿的孪生深度学习与散射小波技术，我们构建了一个为安全高效签名验证系统铺平道路的稳健框架。