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Deep Generative Attacks and Countermeasures for Data-Driven Offline Signature Verification

arXiv 2312.00987 ↗First posted 2 Dec 2023cs.CV cs.CYcited by 0

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Abstract (arXiv)

This study investigates the vulnerabilities of data-driven offline signature verification (DASV) systems to generative attacks and proposes robust countermeasures. Specifically, we explore the efficacy of Variational Autoencoders (VAEs) and Conditional Generative Adversarial Networks (CGANs) in creating deceptive signatures that challenge DASV systems. Using the Structural Similarity Index (SSIM) to evaluate the quality of forged signatures, we assess their impact on DASV systems built with Xception, ResNet152V2, and DenseNet201 architectures. Initial results showed False Accept Rates (FARs) ranging from 0% to 5.47% across all models and datasets. However, exposure to synthetic signatures significantly increased FARs, with rates ranging from 19.12% to 61.64%. The proposed countermeasure, i.e., retraining the models with real + synthetic datasets, was very effective, reducing FARs between 0% and 0.99%. These findings emphasize the necessity of investigating vulnerabilities in security systems like DASV and reinforce the role of generative methods in enhancing the security of data-driven systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 49 references that are in the map. First posted on arXiv 2 Dec 2023.

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20 of 49 references.

References · 20

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
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  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  5. 2013Intriguing properties of neural networkscited by 5,114
  6. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  7. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  8. 2014Conditional Generative Adversarial Netscited by 2,152
  9. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  10. 2020Differentiable Augmentation for Data-Efficient GAN Trainingcited by 361
  11. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  12. 2017SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verificationcited by 31
8 more
  1. 2020DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representationscited by 26
  2. 2017Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networkscited by 20
  3. 2015Offline Handwritten Signature Verification - Literature Reviewcited by 15
  4. 2019Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verificationcited by 9
  5. 2020On the Resilience of Biometric Authentication Systems against Random Inputscited by 6
  6. 2020Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentationcited by 3
  7. 2022Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verificationcited by 2
  8. 2022iCTGAN--An Attack Mitigation Technique for Random-vector Attack on Accelerometer-based Gait Authentication Systemscited by 2

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