Block Induced Signature Generative Adversarial Network (BISGAN): Signature Spoofing Using GANs and Their Evaluation

• URL: http://arxiv.org/abs/2410.06041v2
• Date: Fri, 11 Oct 2024 08:14:58 GMT
• Title: Block Induced Signature Generative Adversarial Network (BISGAN): Signature Spoofing Using GANs and Their Evaluation
• Authors: Haadia Amjad, Kilian Goeller, Steffen Seitz, Carsten Knoll, Naseer Bajwa, Ronald Tetzlaff, Muhammad Imran Malik,
• Abstract summary: Generative adversarial networks (GANs) learn from original and forged signatures to generate forged signatures. This work focuses on creating a generator that produces forged samples that achieve a benchmark in spoofing signature verification systems.
• Score: 1.0177118388531325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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