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https://doi.org/10.62836/jcmea.v4i1.040107
Jiahuai Ma, & Xiaoyang Chen. (2024). Fingerprint Image Generation Based on Attention-Based Deep Generative Adversarial Networks and Its Application in Deep Siamese Matching Model Security Validation. Journal of Computational Methods in Engineering Applications, 4(1), 1–13. https://doi.org/10.62836/jcmea.v4i1.040107
Volume 4, Issue 1, 2024

Fingerprint Image Generation Based on Attention-Based Deep Generative Adversarial Networks and Its Application in Deep Siamese Matching Model Security Validation

This study addresses the critical need to evaluate the security of deep learning models in fingerprint recognition systems, by testing their vulnerability to misidentification. While deep learning techniques have significantly advanced biometric authentication, the potential for misclassification and unauthorized access due to synthetic fingerprints has not been thoroughly investigated. To this end, we propose an enhanced Deep Convolutional Generative Adversarial Network (DCGAN) with attention mechanisms to generate realistic synthetic fingerprint images. These images are then used to test the robustness and security of a Siamese Network employed for fingerprint matching. Experimental results demonstrate that the AE-DCGAN model outperforms traditional DCGANs in image quality and precision, achieving higher accuracy in generating realistic fingerprint textures. Additionally, the Siamese Network, when tested with synthetic fingerprints, reveals certain vulnerabilities, highlighting potential risks in security. Grad-CAM visualizations are employed to further understand the model's attention during fingerprint matching, providing insights into how the model focuses on key fingerprint features. The proposed approach aims to investigate both the generation and recognition phases, contributing to improved robustness and reliability in fingerprint-based systems.

component; DCGAN; fingerprint generation; attention mechanism; Siamese Network

References

  1. Zhang H, Zhu D. Gan Y, et al. End-to-End Learning-Based Study on the Mamba-ECANet Model for Data Security Intrusion Detection. Journal of Information, Technology and Policy 2024; 2(1): 1–17.
  2. Zhou Z, Wu J, Cao Z, et al. On-Demand Trajectory Prediction Based on Adaptive Interaction Car Following Model with Decreasing Tolerance. In Proceedings of the 2021 International Conference on Computers and Automation (CompAuto), Paris, France, 7–9 September 2021; pp. 67–72.
  3. Zhu D, Gan Y, Chen X. Domain Adaptation-Based Machine Learning Framework for Customer Churn Prediction Across Varing Distributions. Journal of Computational Methods in Engineering Applications 2021; 1(1): 1–14.
  4. Gan Y, Zhu D. The Research on Intelligent News Advertisement Recommendation Algorithm Based on Prompt Learning in End-to-End Large Language Model Architecture. Innovations in Applied Engineering and Technology 2024; 3(1): 1–19.
  5. Chen X, Gan Y, Xiong S. Optimization of Mobile Robot Delivery System Based on Deep Learning. Journal of Computer Science Research 2024; 6(4): 51–65.
  6. Liu N, Gu H, Wei Y, et al. Performance Enhancement of AlGaN-Based Deep Ultraviolet Light-Emitting Diodes by Using Stepped And Super-Lattice N-Type Confinement Layer. Superlattices and Microstructures 2020; 141: 106492.
  7. Jain A, Ross A, Prabhakar S. Fingerprint Matching Using Minutiae and Texture Features. In Proceedings of the 2001 International Conference on Image Processing (Cat. No. 01CH37205), Thessaloniki, Greece, 7–10 October 2001; Volume 3, pp. 282–285.
  8. Xiong S, Chen X, Zhang H. Deep Learning-Based Multifunctional End-to-End Model for Optical Character Classification and Denoising. Journal of Computational Methods in Engineering Applications 2023; 3(1): 1–13.
  9. Xiong S, Zhang H, Wang M. Ensemble Model of Attention Mechanism-Based DCGAN and Autoencoder for Noised OCR Classification. Journal of Electronic & Information Systems 2022; 4(1): 33–41.
  10. Tang Y, Li C. Exploring the Factors of Supply Chain Concentration in Chinese A-Share Listed Enterprises. Journal of Computational Methods in Engineering Applications 2023; 3(1): 1–17.
  11. Xiong S, Zhang H. A Multi-Model Fusion Strategy for Android Malware Detection Based on Machine Learning Algorithms. Journal of Computer Science Research 2024; 6(2): 1–11.
  12. Engelsma JJ, Cao K, Jain AK. Learning a Fixed-Length Fingerprint Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2019; 43(6): 1981–1997.
  13. Cao K, Jain AK. Automated Latent Fingerprint Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2018; 41(4): 788–800.
  14. Grosz SA, Engelsma JJ, Jain AK. On the Current State of Cross-Domain Fingerprint Recognition; Michigan State University: East Lansing, MI, USA, 2022.
  15. Cappelli R, Erol A, Maio D, et al. Synthetic Fingerprint-Image Generation. In Proceedings of the 15th International Conference on Pattern Recognition. ICPR-2000, Barcelona, Spain, 3–7 September 2000; Volume 3, pp. 471–474.
  16. Xiong S, Chen X, Zhang H, et al. Domain Adaptation-Based Deep Learning Framework for Android Malware Detection Across Diverse Distributions. Artificial Intelligence Advances 2024; 6(1): 13–24.
  17. Xiong S, Yu L, Shen H, et al. Efficient Algorithms for Sensor Deployment and Routing in Sensor Networks for Network-Structured Environment Monitoring. In Proceedings of the 2012 Proceedings IEEE INFOCOM, Orlando, FL, USA, 25–30 March 2012; pp. 1008–1016.
  18. Yu L, Li J, Cheng S, et al. Secure Continuous Aggregation in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems 2013; 25(3): 762–774.
  19. Chen X, Wang M, Zhang H. Machine Learning–based Fault Prediction and Diagnosis of Brushless Motors. Engineering Advances 2024; 4(3): 130–142.
  20. Engelsma JJ, Grosz S, Jain AK. Printsgan: Synthetic Fingerprint Generator. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022; 45(5): 6111–6124.
  21. Radford A. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv 2015, arXiv:1511.06434.
  22. Chowdhury A, Kirchgasser S, Uhl A, et al. Can a CNN Automatically Learn the Significance of Minutiae Points for Fingerprint Matching. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA, 1–5 March 2020; pp. 351–359.
  23. Wang Z, Zhao Y, Song C, et al. A New Interpretation on Structural Reliability Updating with Adaptive Batch Sampling-Based Subset Simulation. Structural and Multidisciplinary Optimization 2024; 67(1): 7.
  24. Ye X, Luo K, Wang H, et al. An Advanced AI-Based Lightweight Two-Stage Underwater Structural Damage Detection Model. Advanced Engineering Informatics 2024; 62: 102553.
  25. Xiong S, Li, J. An Efficient Algorithm for Cut Vertex Detection in Wireless Sensor Networks. In Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems, Genoa, Italy, 21–25 June 2010; pp. 368–377.
  26. Wang H, Li J, Xiong S. Efficient Join Algorithms for Distributed Information Integration Based on XML. International Journal of Business Process Integration and Management 2008; 3(4): 271–281.
  27. Zeng F, Hu S, Xiao K. Research on Partial Fingerprint Recognition Algorithm Based on Deep Learning. Neural Computing and Applications 2019; 31(9): 4789–4798.
  28. Qiu Y, Chen H, Dong X, et al. Ifvit: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer. arXiv 2024, arXiv:2404.08237.
  29. Althabhawee AFY, Alwawi BKOC. Fingerprint Recognition Based on Collected Images Using Deep Learning Technology. IAES International Journal of Artificial Intelligence 2022; 11(1): 81.
  30. Li J, Xiong S. Efficient Pr-Skyline Query Processing and Optimization in Wireless Sensor Networks. Wireless Sensor Network 2010; 2(11): 838.
  31. Feng Z, Xiong S, Cao D, et al. HRS: A Hybrid Framework for Malware Detection. In Proceedings of the 2015 ACM International Workshop on International Workshop on Security and Privacy Analytics, San Antonio, TX, USA, 4 March 2015; pp. 19–26.
  32. Zhao Y, Dai W, Wang Z, et al. Application of Computer Simulation to Model Transient Vibration Responses of GPLs Reinforced Doubly Curved Concrete Panel under Instantaneous Heating. Materials Today Communications 2024; 38: 107949.
  33. Wenjun D, Fatahizadeh M, Touchaei HG, et al. Application of Six Neural Network-Based Solutions on Bearing Capacity of Shallow Footing on Double-Layer Soils. Steel and Composite Structures 2023; 49(2): 231–244.
  34. Xiong S, Zhang H, Wang M, et al. Distributed Data Parallel Acceleration-Based Generative Adversarial Network for Fingerprint Generation. Innovations in Applied Engineering and Technology 2022; 1(1): 1–12.
  35. Fahim MANI, Jung HY. A Lightweight GAN Network for Large Scale Fingerprint Generation. IEEE Access 2020; 8: 92918–92928.
  36. Minaee S, Abdolrashidi A. Finger-GAN: Generating Realistic Fingerprint Images Using Connectivity Imposed GAN. arXiv 2018, arXiv:1812.10482.
  37. Hao Y, Chen Z, Jin J, et al. Joint Operation Planning of Drivers and Trucks for Semi-Autonomous Truck Platooning. Transportmetrica A: Transport Science 2023; 1–37.
  38. Hao Y, Chen Z, Sun X, et al. Planning of Truck Platooning for Road-Network Capacitated Vehicle Routing Problem. arXiv 2024, arXiv:2404.13512.
  39. Zhu Y, Zhao Y, Song C, et al. Evolving Reliability Assessment of Systems Using Active Learning-Based Surrogate Modelling. Physica D: Nonlinear Phenomena 2024; 457: 133957.
  40. Hu J, Shen L, Sun G. Squeeze-and-Excitation Networks. In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–22 June 2018; pp. 7132–7141.
  41. He A, Luo C., Tian X, et al. A Twofold Siamese Network for Real-Time Object Tracking. In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–22 June 2018; pp. 4834–4843.
  42. Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv 2014, arXiv:1409.1556.
  43. Howard AG, Zhu M, Chen B, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017). arXiv 2017, arXiv:1704.04861.
  44. Tammina S. Transfer Learning Using Vgg-16 with Deep Convolutional Neural Network for Classifying Images. International Journal of Scientific and Research Publications (IJSRP) 2019; 9(10): 143–150.
  45. Vedaldi A, Zisserman A. Vgg Convolutional Neural Networks Practical; Department of Engineering Science, University of Oxford: Oxford, UK, 2016; Volume 66.
  46. Sinha D, El-Sharkawy M. Thin Mobilenet: An Enhanced Mobilenet Architecture. In Proceedings of the 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, 10–12 October 2019.
  47. Selvaraju RR, Das A, Vedantam R, et al. Grad-CAM: Why Did You Say That?arXiv 2016, arXiv:1611.07450.
  48. Selvaraju RR, Cogswell M, Das A, et al. Grad-Cam: Visual Explanations from Deep Networks via Gradient-Based Localization. In Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017; pp. 618–626.
  49. Chen L, Chen J, Hajimirsadeghi H, et al. Adapting Grad-Cam for Embedding Networks. In Proceedings of the 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA, 1–5 March 2020; pp. 2794–2803.

Supporting Agencies

  1. Funding: Not applicable.