AuthSig: Safeguarding Scanned Signatures Against Unauthorized Reuse in Paperless Workflows

2025-11-12

AI Summary

Static scanned signatures in paperless office environments are vulnerable to unauthorized copying, reuse, and lack fine-grained usage control. Method: This paper proposes AuthSig—a novel framework that integrates implicit watermarking with generative modeling. It introduces a differentiable steganographic method based on style encoding to embed fine-grained, one-time authentication information into signature images, augmented by keypoint-driven data augmentation. Leveraging human visual insensitivity to stylistic perturbations, the method achieves perceptually consistent watermark embedding while preserving visual authenticity. Contribution/Results: Experiments demonstrate >98% watermark extraction accuracy under digital distortions and cross-domain print-scan attacks. AuthSig enables a strong “one signature, one-time use” authentication policy, effectively endowing static signatures with dynamic security properties.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Privacy
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Preventing unauthorized reuse of scanned signatures in paperless workflows
• Addressing vulnerability of static signature images to malicious copying
• Ensuring reliable authentication through watermark-based signature protection

Innovation

• Generative models embed watermarks into signature images
• Keypoint-driven augmentation enhances style diversity for robustness
• One Signature One Use policy prevents unauthorized copying