CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting
  Authentication
Jingyao Wang Luntian Mou Changwen Zheng Wen Gao
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.