Skip to main content
SpringerLink
Log in

Exploiting vulnerability of convolutional neural network-based gait recognition system

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In today’s era of advanced technologies, the concerns related to global security have led to video surveillance gadgets. Human gait recognition as a biometric is considered an evolving technology for intelligent surveillance monitoring. This research study exploits vulnerabilities associated with a convolutional neural network (CNN)-based gait recognition system under various walking conditions involving clothing, carrying items, and speed. In the first stage, we design a CNN model capable of identifying individuals based on their gait characteristics. Subsequently, in the next stage, we design a five-pixel adversarial attack in which we perturb the gait features of individuals computed using the fast gradient sign method. The resulting perturbation is added to only five random pixels to create naturalistic adversarial samples similar to the original samples. Further, the main aim of this study is to determine and analyze the performance of the CNN-based gait recognition system under an adversarial attack. The research concludes that gait recognition systems based on CNN models are highly susceptible to adversarial attacks, motivating researchers to design defense mechanisms to mitigate the effect of these attacks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The datasets used in this research are publicly available, and datasets can be made available by contacting the corresponding author.

References

  1. Galvez RL, Bandala AA, Dadios EP, Vicerra RRP, Maningo JMZ (2018) Object detection using convolutional neural networks. In: TENCON 2018–2018 IEEE Region 10 Conference, IEEE, pp 2023–2027

  2. Sun W, Wang R (2018) Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with DSM. IEEE Geosci Remote Sens Lett 15(3):474–478

    Article  Google Scholar 

  3. Tian Y, Cheng G, Gelernter J, Yu S, Song C, Yang B (2020) Joint temporal context exploitation and active learning for video segmentation. Pattern Recogn 100:107158

    Article  Google Scholar 

  4. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  5. Liu F, Shen C, Lin G (2015) Deep convolutional neural fields for depth estimation from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5162–5170

  6. Maqsood M, Yasmin S, Mehmood I, Bukhari M, Kim M (2021) An Efficient DA-net architecture for lung nodule segmentation. Mathematics 9(13):1457

    Article  Google Scholar 

  7. Maqsood M, Bukhari M, Ali Z, Gillani S, Mehmood I, Rho S, Jung Y (2021) A residual-learning-based multi-scale parallel-convolutions-assisted efficient CAD system for liver tumor detection. Mathematics 9(10):1133

    Article  Google Scholar 

  8. Ali Z, Irtaza A, Maqsood M (2021) An IOMT assisted lung nodule segmentation using enhanced receptive field-based modified UNet. Pers Ubiquitous Comput:1–15

  9. Nawaz H, Maqsood M, Afzal S, Aadil F, Mehmood I, Rho S (2020) A deep feature-based real-time system for Alzheimer disease stage detection. Multimed Tools Appl:1–19

  10. Ramadhani AM, Goo HS (2017) Twitter sentiment analysis using deep learning methods. In: 2017 7th International Annual Engineering Seminar (InAES), IEEE, pp 1–4

  11. Zhang Z, He Q, Gao J, Ni M (2018) A deep learning approach for detecting traffic accidents from social media data. Transp Res Part C Emerging Technol 86:580–596

    Article  Google Scholar 

  12. Yasir M, Durrani MY, Afzal S, Maqsood M, Aadil F, Mehmood I, Rho S (2019) An intelligent event-sentiment-based daily foreign exchange rate forecasting system. Appl Sci 9(15):2980

    Article  Google Scholar 

  13. Yuan L, Qu Z, Zhao Y, Zhang H, Nian Q (2017) A convolutional neural network based on TensorFlow for face recognition. In: 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), IEEE, pp 525–529

  14. Al-Waisy AS, Qahwaji R, Ipson S, Al-Fahdawi S, Nagem TA (2018) A multi-biometric iris recognition system based on a deep learning approach. Pattern Anal Appl 21(3):783–802

    Article  MathSciNet  Google Scholar 

  15. Wani MA, Bhat FA, Afzal S, Khan AI (2020) Supervised deep learning in fingerprint recognition. In: Advances in Deep Learning. Springer, pp 111–132

  16. Lee TK, Belkhatir M, Sanei S (2014) A comprehensive review of past and present vision-based techniques for gait recognition. Multimed Tools Appl 72(3):2833–2869

    Article  Google Scholar 

  17. Yang SX, Larsen PK, Alkjær T, Simonsen EB, Lynnerup N (2014) Variability and similarity of gait as evaluated by joint angles: implications for forensic gait analysis. J For Sci 59(2):494–504

    Google Scholar 

  18. BenAbdelkader C, Cutler R, Davis L (2002) Stride and cadence as a biometric in automatic person identification and verification. In: Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, IEEE, pp 372–377

  19. Alotaibi M, Mahmood A (2017) Improved gait recognition based on specialized deep convolutional neural network. Comput Vis Image Underst 164:103–110

    Article  Google Scholar 

  20. Hawas AR, El-Khobby HA, Abd-Elnaby M, Abd El-Samie FE (2019) Gait identification by convolutional neural networks and optical flow. Multimed Tools Appl 78(18):25873–25888

    Article  Google Scholar 

  21. Linda GM, Themozhi G, Bandi SR (2020) Color-mapped contour gait image for cross-view gait recognition using deep convolutional neural network. Int J Wavelets Multiresolut Inf Process 18(01):1941012

    Article  MathSciNet  MATH  Google Scholar 

  22. Arshad H, Khan MA, Sharif MI, Yasmin M, Tavares JMR, Zhang YD, Satapathy SC (2020) A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition. Expert Systems:e12541

  23. Wu X, Yang T, Xia Z (2020) Gait recognition based on densenet transfer learning. Int J Sci Environ 9(1):1–14

    Google Scholar 

  24. Zhu Z-A, Lu Y-Z, Chiang C-K (2019) Generating adversarial examples by makeup attacks on face recognition. In: 2019 IEEE International Conference on Image Processing (ICIP), IEEE, pp 2516–2520

  25. Dong Y, Su H, Wu B, Li Z, Liu W, Zhang T, Zhu J (2019) Efficient decision-based black-box adversarial attacks on face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 7714–7722

  26. Abiew NAK, Jnr MD, Banning SO (2020) Design and implementation of cost effective multi-factor authentication framework for ATM systems. Asian J Res Comput Sci:7–20

  27. Kotia J, Kotwal A, Bharti R (2019) Risk susceptibility of brain tumor classification to adversarial attacks. In: International Conference on Man–Machine Interactions, Springer, pp 181–187

  28. Ma X, Niu Y, Gu L, Wang Y, Zhao Y, Bailey J, Lu F (2021) Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recogn 110:107332

    Article  Google Scholar 

  29. Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller P-A (2019) Adversarial attacks on deep neural networks for time series classification. In: 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1–8

  30. Zhou Z, Guan H, Bhat MM, Hsu J (2019) Fake news detection via NLP is vulnerable to adversarial attacks. Preprint https://arxiv.org/abs/190109657

  31. Xu J (2021) A deep learning approach to building an intelligent video surveillance system. Multimed Tools Appl 80(4):5495–5515

    Article  Google Scholar 

  32. Jung S, Lee H, Hwang S, Shim DH (2018) Real time embedded system framework for autonomous drone racing using deep learning techniques. In: 2018 AIAA Information Systems-AIAA Infotech@ Aerospace, p 2138

  33. Boles A, Rad P (2017) Voice biometrics: deep learning-based voiceprint authentication system. In: 2017 12th System of Systems Engineering Conference (SoSE), IEEE, pp 1–6

  34. Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. Preprint https://arxiv.org/abs/14126572

  35. Su J, Vargas DV, Sakurai K (2019) One pixel attack for fooling deep neural networks. IEEE Trans Evol Comput 23(5):828–841

    Article  Google Scholar 

  36. Chen P-Y, Zhang H, Sharma Y, Yi J, Hsieh C-J (2017) Zoo: zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp 15–26

  37. Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2013) Intriguing properties of neural networks. Preprint https://arxiv.org/abs/13126199

  38. Kurakin A, Goodfellow I, Bengio S (2016) Adversarial examples in the physical world

  39. Papernot N, McDaniel P, Jha S, Fredrikson M, Celik ZB, Swami A (2016) The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P), IEEE, pp 372–387

  40. Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (sp), IEEE, pp 39–57

  41. Liu Y, Chen X, Liu C, Song D (2016) Delving into transferable adversarial examples and black-box attacks. Preprint https://arxiv.org/abs/161102770

  42. Li Y, Zhang H, Bermudez C, Chen Y, Landman BA, Vorobeychik Y (2020) Anatomical context protects deep learning from adversarial perturbations in medical imaging. Neurocomputing 379:370–378

    Article  Google Scholar 

  43. Cheng G, Ji H (2020) Adversarial perturbation on MRI modalities in brain tumor segmentation. IEEE Access 8:206009–206015

    Article  Google Scholar 

  44. Roth T, Gao Y, Abuadbba A, Nepal S, Liu W (2021) Token-modification adversarial attacks for natural language processing: a survey. Preprint https://arxiv.org/abs/210300676

  45. Neekhara P, Hussain S, Pandey P, Dubnov S, McAuley J, Koushanfar F (2019) Universal adversarial perturbations for speech recognition systems. Preprint https://arxiv.org/abs/190503828

  46. Xie S, Wang H, Kong Y, Hong Y (2021) Universal 3-Dimensional perturbations for black-box attacks on video recognition systems. Preprint https://arxiv.org/abs/210704284

  47. Kuppa A, Grzonkowski S, Asghar MR, Le-Khac N-A (2019) Black box attacks on deep anomaly detectors. In: Proceedings of the 14th International Conference on Availability, Reliability and Security, pp 1–10

  48. Yuan X, He P, Zhu Q, Li X (2019) Adversarial examples: attacks and defenses for deep learning. IEEE Trans Neural Netw Learn Syst 30(9):2805–2824

    Article  MathSciNet  Google Scholar 

  49. Papernot N, McDaniel P, Wu X, Jha S, Swami A (2016) Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE Symposium on Security and Privacy (SP), IEEE, pp 582–597

  50. Huang R, Xu B, Schuurmans D, Szepesvári C (2015) Learning with a strong adversary. Preprint https://arxiv.org/abs/151103034

  51. Liang B, Li H, Su M, Li X, Shi W, Wang X (2018) Detecting adversarial image examples in deep neural networks with adaptive noise reduction. IEEE Transactions on Dependable and Secure Computing

  52. Carlini N, Wagner D (2017) Adversarial examples are not easily detected: Bypassing ten detection methods. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp 3–14

  53. Carlini N, Wagner D (2016) Defensive distillation is not robust to adversarial examples. Preprint https://arxiv.org/abs/160704311

  54. Bukhari M, Bajwa KB, Gillani S, Maqsood M, Durrani MY, Mehmood I, Ugail H, Rho S (2020) An efficient gait recognition method for known and unknown covariate conditions. IEEE Access

  55. Yu S, Tan D, Tan TA (2006) framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th International Conference on Pattern Recognition (ICPR'06), IEEE, pp 441–444

Download references

Acknowledgements

This work was supported in part by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE), The Competency Development Program for Industry Specialist, under Grant P0008703, and also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1060668).

Author information

Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, Pakistan

    Maryam Bukhari, Mehr Yahya Durrani & Sadaf Yasmin

  2. Department of Computer Science, Bahria University, Lahore, Pakistan

    Saira Gillani

  3. Department of Industrial Security, Chung-Ang University, Seoul, 06974, South Korea

    Seungmin Rho

  4. Department of Computer Engineering, Mokwon University, Daejeon, 35349, South Korea

    Sang-Soo Yeo

Authors
  1. Maryam Bukhari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehr Yahya Durrani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saira Gillani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sadaf Yasmin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seungmin Rho
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sang-Soo Yeo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sang-Soo Yeo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bukhari, M., Durrani, M.Y., Gillani, S. et al. Exploiting vulnerability of convolutional neural network-based gait recognition system. J Supercomput 78, 18578–18597 (2022). https://doi.org/10.1007/s11227-022-04611-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04611-3

Keywords

Search

Navigation