# SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature Verification

arXiv: https://arxiv.org/abs/2201.10138 · HTML: https://mlsanity.com/paper/2201.10138/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Self-supervised learning methods and applications in medical imaging analysis: A survey](https://mlsanity.com/paper/2109.08685/) (2021)
- [Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting](https://mlsanity.com/paper/2103.13716/) (2021)
- [SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification](https://mlsanity.com/paper/1707.02131/) (2017)
- [Offline Handwritten Signature Verification - Literature Review](https://mlsanity.com/paper/1507.07909/) (2015)
- [Signature Verification Approach using Fusion of Hybrid Texture Features](https://mlsanity.com/paper/1709.09348/) (2017)
- [Signature Verification using Geometrical Features and Artificial Neural Network Classifier](https://mlsanity.com/paper/2108.02029/) (2021)

## Built on by (most cited first, 8 of 8)

- [Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning](https://mlsanity.com/paper/2408.09676/) (2024)
- [CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication](https://mlsanity.com/paper/2307.11100/) (2023)
- [Multiscale Feature Learning Using Co-Tuplet Loss for Offline Handwritten Signature Verification](https://mlsanity.com/paper/2308.00428/) (2023)
- [SigScatNet: A Siamese + Scattering based Deep Learning Approach for Signature Forgery Detection and Similarity Assessment](https://mlsanity.com/paper/2311.05579/) (2023)
- [Self-Supervised Learning Based Handwriting Verification](https://mlsanity.com/paper/2405.18320/) (2024)
- [Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification](https://mlsanity.com/paper/2410.01441/) (2024)
- [Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification](https://mlsanity.com/paper/2308.01136/) (2023)
- [DetailSemNet: Elevating Signature Verification through Detail-Semantic Integration](https://mlsanity.com/paper/2511.16364/) (2025)

## Abstract (arXiv)

Offline Signature Verification (OSV) is a fundamental biometric task across various forensic, commercial and legal applications. The underlying task at hand is to carefully model fine-grained features of the signatures to distinguish between genuine and forged ones, which differ only in minute deformities. This makes OSV more challenging compared to other verification problems. In this work, we propose a two-stage deep learning framework that leverages self-supervised representation learning as well as metric learning for writer-independent OSV. First, we train an image reconstruction network using an encoder-decoder architecture that is augmented by a 2D spatial attention mechanism using signature image patches. Next, the trained encoder backbone is fine-tuned with a projector head using a supervised metric learning framework, whose objective is to optimize a novel dual triplet loss by sampling negative samples from both within the same writer class as well as from other writers. The intuition behind this is to ensure that a signature sample lies closer to its positive counterpart compared to negative samples from both intra-writer and cross-writer sets. This results in robust discriminative learning of the embedding space. To the best of our knowledge, this is the first work of using self-supervised learning frameworks for OSV. The proposed two-stage framework has been evaluated on two publicly available offline signature datasets and compared with various state-of-the-art methods. It is noted that the proposed method provided promising results outperforming several existing pieces of work. The code is publicly available at: https://github.com/soumitri2001/SURDS-SSL-OSV