Block Induced Signature Generative Adversarial Network (BISGAN): Signature Spoofing Using GANs and Their Evaluation
<|reference_start|>Block Induced Signature Generative Adversarial Network (BISGAN): Signature Spoofing Using GANs and Their Evaluation: Deep learning is actively being used in biometrics to develop efficient identification and verification systems. Handwritten signatures are a common subset of biometric data for authentication purposes. Generative adversarial networks (GANs) learn from original and forged signatures to generate forged signatures. While most GAN techniques create a strong signature verifier, which is the discriminator, there is a need to focus more on the quality of forgeries generated by the generator model. This work focuses on creating a generator that produces forged samples that achieve a benchmark in spoofing signature verification systems. We use CycleGANs infused with Inception model-like blocks with attention heads as the generator and a variation of the SigCNN model as the base Discriminator. We train our model with a new technique that results in 80% to 100% success in signature spoofing. Additionally, we create a custom evaluation technique to act as a goodness measure of the generated forgeries. Our work advocates generator-focused GAN architectures for spoofing data quality that aid in a better understanding of biometric data generation and evaluation.<|reference_end|>
@article{amjad2024block,
    title={Block Induced Signature Generative Adversarial Network (BISGAN):
  Signature Spoofing Using GANs and Their Evaluation},
    author={Haadia Amjad, Kilian Goeller, Steffen Seitz, Carsten Knoll, Naseer
  Bajwa, Ronald Tetzlaff and Muhammad Imran Malik},
    journal={arXiv preprint arXiv:2410.06041},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06041},
    primaryClass={cs.CV cs.AI}
}