AuthSig: Safeguarding Scanned Signatures Against Unauthorized Reuse in Paperless Workflows
On the map: LLM safety and security → Watermarking generated content and models
Abstract (arXiv)
With the deepening trend of paperless workflows, signatures as a means of identity authentication are gradually shifting from traditional ink-on-paper to electronic formats.Despite the availability of dynamic pressure-sensitive and PKI-based digital signatures, static scanned signatures remain prevalent in practice due to their convenience. However, these static images, having almost lost their authentication attributes, cannot be reliably verified and are vulnerable to malicious copying and reuse. To address these issues, we propose AuthSig, a novel static electronic signature framework based on generative models and watermark, which binds authentication information to the signature image. Leveraging the human visual system's insensitivity to subtle style variations, AuthSig finely modulates style embeddings during generation to implicitly encode watermark bits-enforcing a One Signature, One Use policy.To overcome the scarcity of handwritten signature data and the limitations of traditional augmentation methods, we introduce a keypoint-driven data augmentation strategy that effectively enhances style diversity to support robust watermark embedding. Experimental results show that AuthSig achieves over 98% extraction accuracy under both digital-domain distortions and signature-specific degradations, and remains effective even in print-scan scenarios.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security. First posted on arXiv 12 Nov 2025.
Builds on
0 of 0 references.
No references here yet.
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026DeepSignature: Digitally Signed, Content-Encoding Watermarks for Robust and Transparent Image Authentication
- 2026MarkNull: Model-Agnostic Watermark Removal in AI-Generated Images via On-Manifold Latent Manipulation
- 2025MetaSeal: Defending Against Image Attribution Forgery Through Content-Dependent Cryptographic Watermarks
- 2023The Stable Signature: Rooting Watermarks in Latent Diffusion Models
- 2024RoboSignature: Robust Signature and Watermarking on Network Attacks
- 2025ImageSentinel: Protecting Visual Datasets from Unauthorized Retrieval-Augmented Image Generation
- 2025A Watermark for Auto-Regressive Image Generation Models
- 2025Mitigating Watermark Forgery in Generative Models via Randomized Key Selection