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Self-Supervised Diffusion Models for Handwritten Signature Verification via Ruin Reconstruction — arXiv:2611.07810

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.07810
License (as archived): CC0-1.0
Archived: 2026-11-06T14:53:34.090Z
SHA-256 of the archived text: c41c46c6c16082523e8c6bab82ee4c3f104ec6d4baf7161968f91b106e2181c2
Stable URL of this page: https://metraipolis.com/documento/dc_ae558155ea4c

How to cite

Self-Supervised Diffusion Models for Handwritten Signature Verification via Ruin Reconstruction — arXiv:2611.07810 — arXiv (en), https://arxiv.org/abs/2611.07810 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ae558155ea4c (Metraipolis archive dc_ae558155ea4c, sha256 c41c46c6c16082523e8c6bab82ee4c3f104ec6d4baf7161968f91b106e2181c2)

BibTeX

@misc{dcae558155ea4c,
  title = {Self-Supervised Diffusion Models for Handwritten Signature Verification via Ruin Reconstruction — arXiv:2611.07810},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.07810},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ae558155ea4c; sha256 c41c46c6c16082523e8c6bab82ee4c3f104ec6d4baf7161968f91b106e2181c2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.07810. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Handwritten signature verification (HSV) is a critical biometric technique in legal and forensic authentication, whose efficacy hinges on the discriminability of signature representations. Current HSV methods predominantly adopt supervised learning to enhance representation quality, leaving self-supervised learning largely unexplored. We propose \emph{SigDiffuser}, a diffusion-based self-supervised framework for signature representation and verification. At its core, \emph{SigDiffuser} introduces a novel pretext task termed \emph{Reconstruction from Ruins (RFR)}, which deliberately ``ruins'' signatures through severe crumpling, obliterating their structural integrity and writing styles. A visual encoder is employed to capture crucial handwriting patterns from intact signature regions, guiding the denoiser to reconstruct the ``ruined'' portions and learning meaningful representations. Additionally, we introduce a \emph{pyramidal feature aggregation module (PFAM)} to bridge the visual encoder and denoiser, enhancing representation quality via hierarchical aggregation and modulation. Extensive experiments on multilingual signature datasets demonstrate that \emph{SigDiffuser} significantly outperforms both state-of-the-art SSL methods and HSV models. We further show that increasing the crumpling severity on signatures effectively improves representation quality. Notably, our work demonstrates that extreme destruction, coupled with self-supervised diffusion models, can yield superior signature representation compared to traditional mild deformations, paving new avenues for HSV and other biometric domains. Code will be publicly available.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.07810 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-06T14:25:42.618Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).