Summary of Block Induced Signature Generative Adversarial Network (bisgan): Signature Spoofing Using Gans and Their Evaluation, by Haadia Amjad et al.
Block Induced Signature Generative Adversarial Network (BISGAN): Signature Spoofing Using GANs and Their Evaluation
by Haadia Amjad, Kilian Goeller, Steffen Seitz, Carsten Knoll, Naseer Bajwa, Ronald Tetzlaff, Muhammad Imran Malik
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Generative adversarial networks (GANs) are being used to develop efficient identification and verification systems in biometrics, specifically for handwritten signatures. This paper focuses on improving the quality of forged signature samples generated by GANs, which can aid in spoofing signature verification systems. The authors propose a CycleGAN-based generator infused with Inception model-like blocks and attention heads, paired with a SigCNN-based discriminator. They train their model using a novel technique, achieving 80-100% success in signature spoofing. Additionally, they introduce a custom evaluation metric to measure the quality of generated forgeries. This work highlights the importance of generator-focused GAN architectures for improving biometric data generation and evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about using computers to create fake handwritten signatures that can fool signature verification systems. The goal is to make these fake signatures as realistic as possible, so they can’t be easily detected as fakes. To do this, the researchers use a special type of computer model called a GAN (Generative Adversarial Network). They modify this model by adding some extra features that help it create more realistic fake signatures. After training their model using a new technique, they found that they could successfully fool signature verification systems 80-100% of the time! This research is important because it helps us understand how to generate and evaluate biometric data, like fingerprints or handwriting. |
Keywords
» Artificial intelligence » Attention » Gan » Generative adversarial network
