Handwriting authentication is a valuable tool used in various fields, such as fraud prevention and cultural heritage protection. However, it remains a challenging task due to the complex features, severe damage, and lack of supervision. In this paper, we propose a novel Contrastive Self-Supervised Learning framework for Robust Handwriting Authentication (CSSL-RHA) to address these issues. It can dynamically learn complex yet important features and accurately predict writer identities. Specifically, to remove the negative effects of imperfections and redundancy, we design an information-theoretic filter for pre-processing and propose a novel adaptive matching scheme to represent images as patches of local regions dominated by more important features. Through online optimization at inference time, the most informative patch embeddings are identified as the "most important" elements. Furthermore, we employ contrastive self-supervised training with a momentum-based paradigm to learn more general statistical structures of handwritten data without supervision. We conduct extensive experiments on five benchmark datasets and our manually annotated dataset EN-HA, which demonstrate the superiority of our CSSL-RHA compared to baselines. Additionally, we show that our proposed model can still effectively achieve authentication even under abnormal circumstances, such as data falsification and corruption.

翻译：手写认证是一种广泛应用于欺诈预防和文化遗产保护等领域的有价值工具。然而，由于复杂特征、严重损坏以及缺乏监督，它仍是一项具有挑战性的任务。在本文中，我们提出了一种新颖的对比自监督学习框架——鲁棒手写认证（CSSL-RHA），以解决这些问题。该框架能够动态学习复杂而重要的特征，并准确预测书写者身份。具体而言，为了消除不完美性和冗余的负面影响，我们设计了一个信息论滤波器用于预处理，并提出了一种新颖的自适应匹配方案，将图像表示为由更重要的特征主导的局部区域补丁。通过在推理时进行在线优化，信息量最大的补丁嵌入被识别为“最重要”的元素。此外，我们采用基于动量范式的对比自监督训练，以在无监督的情况下学习手写数据更通用的统计结构。我们在五个基准数据集和手动标注的EN-HA数据集上进行了广泛实验，结果表明我们的CSSL-RHA相较于基线方法具有优越性。此外，我们展示了即使在数据伪造和损坏等异常情况下，所提出的模型仍能有效实现认证。