IEEE Access
The paper entitled “SignForensics: A Robust Framework for Forensic Offline Signature Verification with Enhanced Detection, Noise Removal, and Multi-Stage Authentication” has been accepted at IEEE Access 
This work presents SignForensics, an innovative framework for forensic offline signature verification. The proposed pipeline integrates YOLOv10 for automatic signature detection in documents, CycleGAN for noise removal such as lines, stamps, and printed text, and customized deep learning models based on SigNet and Capsule Networks for advanced feature extraction. The approach adopts a multi-stage authentication strategy, first assessing the compatibility of a signature with a set of potential signers and then verifying its authenticity. The study provides a significant contribution to the field of multimedia forensics, offering a robust and reliable tool for signature authentication in real-world, noisy, and complex scenarios.
Authors: Giovanni Piccinini, Luca Guarnera, Sebastiano BattiatoLink: https://ieeexplore.ieee.org/document/11192260
