DRSV: A Robust Framework for Signature Verification Based on Diffusion Model

Offline signature verification remains vulnerable to real-world degradations such as low-resolution scans, transmission artifacts, and environmental noise. We introduce a variant robustness mechanism that foregrounds stroke-level evidence, and instantiate it in a Diffusion-based Robust Signature Verification (DRSV) framework. DRSV integrates two components: (i) a diffusion-driven noise-signature relation module that synthesizes plausible variations to enhance structural resilience, and (ii) a transformer-based verifier that fuses the predicted noise with the signature image via bi-directional channel attention to decide authenticity. Experiments on five public datasets (CEDAR, BHSig-Bengali, BHSig-Hindi, GPDS, UTSig) show consistent accuracy gains and improved robustness across diverse perturbations, indicating the effectiveness of the proposed framework.