# Introduction to Presentation Attacks in Signature Biometrics and Recent Advances

arXiv: https://arxiv.org/abs/2302.08320 · HTML: https://mlsanity.com/paper/2302.08320/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 61 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations](https://mlsanity.com/paper/2009.06308/) (2020)
- [DeepSign: Deep On-Line Signature Verification](https://mlsanity.com/paper/2002.10119/) (2020)
- [BiosecurID: a multimodal biometric database](https://mlsanity.com/paper/2111.03472/) (2021)
- [BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks](https://mlsanity.com/paper/2001.10223/) (2020)
- [Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health](https://mlsanity.com/paper/2202.12760/) (2022)
- [SVC-onGoing: Signature Verification Competition](https://mlsanity.com/paper/2108.06090/) (2021)
- [SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification](https://mlsanity.com/paper/1911.05358/) (2019)
- [Biometric Template Storage with Blockchain: A First Look into Cost and Performance Tradeoffs](https://mlsanity.com/paper/1904.13128/) (2019)
- [OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification](https://mlsanity.com/paper/1904.00240/) (2019)
- [Sensor interoperability and fusion in signature verification: A case study using tablet PC](https://mlsanity.com/paper/2210.11139/) (2022)

## Built on by (most cited first, 1 of 1)

- [Exploring Transformers for On-Line Handwritten Signature Verification](https://mlsanity.com/paper/2307.01663/) (2023)

## Abstract (arXiv)

Applications based on biometric authentication have received a lot of interest in the last years due to the breathtaking results obtained using personal traits such as face or fingerprint. However, it is important not to forget that these biometric systems have to withstand different types of possible attacks. This chapter carries out an analysis of different Presentation Attack (PA) scenarios for on-line handwritten signature verification. The main contributions of this chapter are: i) an updated overview of representative methods for Presentation Attack Detection (PAD) in signature biometrics; ii) a description of the different levels of PAs existing in on-line signature verification regarding the amount of information available to the impostor, as well as the training, effort, and ability to perform the forgeries; and iii) an evaluation of the system performance in signature biometrics under different scenarios considering recent publicly available signature databases, DeepSignDB and SVC2021_EvalDB. This work is in line with recent efforts in the Common Criteria standardization community towards security evaluation of biometric systems.