Skip to main content
SpringerLink
Log in

Pattern lock screen detection method based on lightweight deep feature extraction

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In the digital age, many people have used mobile phones, thus, mobile phones are one of the most commonly used crime tools. Users can take security measures to their mobile devices using various authorization methods such as passwords or screen patterns (touch screen security authentication). The used security measures generally make mobile forensic analysis difficult or even impossible. In order to overcome this problem, a novel intelligent pattern lock detector is presented in this research. The proposed lock detector uses transfer learning to extract deep lightweight features, iterative feature chosen function and a shallow classifier. A feature extraction network has been created by using SqueezeNet and MobileNet-V2, which are among the deep learning architectures in this work. Iterative Minimum Redundancy Maximum Relevance (ImRMR) was used for feature selection. Linear discriminant analysis (LDA) was selected for the classifier. The proposed model has been developed on three image datasets. These datasets are named clean, slightly dirty and medium dirty. 99.75%, 98.55% and 96.50% classification accuracies have been reached on the used three datasets, respectively. The findings clearly denote that the success of the presented deep lightweight features and ImRMR-based detector.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

Three new mobile phone screen patterns datasets were collected and they publicly published. The researchers can download the collected datasets using https://www.kaggle.com/omerfarukyak/pattern-lock-screen-detection.

Notes

  1. https://www.kaggle.com/omerfarukyak/pattern-lock-screen-detection.

References

  1. Alendal G, Dyrkolbotn GO, Axelsson S (2018) Forensics acquisition — Analysis and circumvention of samsung secure boot enforced common criteria mode. Digit Investig. https://doi.org/10.1016/j.diin.2018.01.008

    Article  Google Scholar 

  2. Garfinkel SL (2010) Digital forensics research: the next 10 years. Digit Investig. https://doi.org/10.1016/j.diin.2010.05.009

    Article  Google Scholar 

  3. Ibrahim TM, Abdulhamid SM, Alarood AA et al (2019) Recent advances in mobile touch screen security authentication methods: a systematic literature review. Comput Secur 85:1–24

    Article  Google Scholar 

  4. Abdulhamid SM, Waziri VO, Idris I et al (2018) A forensic evidence recovery from mobile device applications. Int J Digit Enterp Technol. https://doi.org/10.1504/ijdet.2018.10013745

    Article  Google Scholar 

  5. Feng T, Liu Z, Kwon KA, et al (2012) Continuous mobile authentication using touchscreen gestures. In: 2012 IEEE International conference on technologies for homeland security, HST 2012

  6. Frank M, Biedert R, Ma E et al (2013) Touchalytics: on the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Trans Inf Foren Secur. https://doi.org/10.1109/TIFS.2012.2225048

    Article  Google Scholar 

  7. Shahzad M, Liu AX, Samuel A (2013) Secure unlocking of mobile touch screen devices by simple gestures–You can see it but you can not do it. In: Proceedings of the annual international conference on mobile computing and networking, MOBICOM

  8. Meng W, Wong DS, Furnell S, Zhou J (2015) Surveying the development of biometric user authentication on mobile phones. IEEE Commun Surv Tutorials. https://doi.org/10.1109/COMST.2014.2386915

    Article  Google Scholar 

  9. Murao K, Tobise H, Terada T et al (2014) Mobile phone user authentication with grip gestures using pressure sensors. In: 12th International conference on advances in mobile computing and multimedia, MoMM 2014

  10. Nader J, Alsadoon A, Prasad PWC et al (2015) Designing touch-based hybrid authentication method for smartphones. In: Procedia computer science

  11. Antal M, Szabó LZ (2016) Biometric authentication based on touchscreen swipe patterns. Procedia Technol. https://doi.org/10.1016/j.protcy.2016.01.061

    Article  Google Scholar 

  12. Andriotis P, Oikonomou G, Mylonas A, Tryfonas T (2016) A study on usability and security features of the android pattern lock screen. Inf Comput Secur. https://doi.org/10.1108/ICS-01-2015-0001

    Article  Google Scholar 

  13. Ng’Ang’A A, Musuva PMW (2020) Enhancing accuracy in a touch operation biometric system: a case on the android pattern lock scheme. Mob Inf Syst. https://doi.org/10.1155/2020/4165457

    Article  Google Scholar 

  14. Ye G et al. (2017) Cracking android pattern lock in five attempts. In: Proceedings of the Network and Distributed System Security Symposium 2017 (NDSS 17). Internet Society

  15. Sedik A, Faragallah OS, El-sayed HS et al (2022) An efficient cybersecurity framework for facial video forensics detection based on multimodal deep learning. Neural Comput Appl 34:1251–1268. https://doi.org/10.1007/s00521-021-06416-6

    Article  Google Scholar 

  16. Nour M, Öztürk Ş, Polat K (2021) A novel classification framework using multiple bandwidth method with optimized CNN for brain–computer interfaces with EEG-fNIRS signals. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06202-4

    Article  Google Scholar 

  17. Zhang Y, Yu W, He L et al (2021) Signals classification based on IA-optimal CNN. Neural Comput Appl. https://doi.org/10.1007/s00521-021-05736-x

    Article  Google Scholar 

  18. Ma Z, Jiang M, Huang W (2020) Trusted forensics scheme based on digital watermark algorithm in intelligent VANET. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04246-1

    Article  Google Scholar 

  19. Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the Inception Architecture for Computer Vision. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition

  20. Iandola FN, Han S, Moskewicz MW et al (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. 1–13

  21. Sandler M, Howard A, Zhu M et al (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 4510–4520

  22. Chen J, Zeb A, Yang S et al (2021) Automatic identification of commodity label images using lightweight attention network. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06081-9

    Article  Google Scholar 

  23. Hammad M, Iliyasu AM, Subasi A et al (2021) A multitier deep learning model for arrhythmia detection. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2020.3033072

    Article  Google Scholar 

  24. Ben Brahim A (2021) Stable feature selection based on instance learning, redundancy elimination and efficient subsets fusion. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04971-y

    Article  Google Scholar 

  25. Toğaçar M, Ergen B, Cömert Z (2020) Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybern Biomed Eng. https://doi.org/10.1016/j.bbe.2019.11.004

    Article  Google Scholar 

  26. Kandala RNVPS, Dhuli R, Pławiak P et al (2019) Towards real-time heartbeat classification: evaluation of nonlinear morphological features and voting method. Sensors (Switzerland). https://doi.org/10.3390/s19235079

    Article  Google Scholar 

  27. Sandler M, Howard A, Zhu M et al (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition

  28. Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 07–12-June:1–9. https://doi.org/10.1109/CVPR.2015.7298594

  29. He K, Zhang X, Ren S, Sun J (2016) ResNet. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR.2016.90

  30. Korfiatis P, Kline TL, Lachance DH et al (2017) Residual deep convolutional neural network predicts MGMT methylation status. J Digit Imaging. https://doi.org/10.1007/s10278-017-0009-z

    Article  Google Scholar 

  31. Kang J, Gwak J (2019) Ensemble of instance segmentation models for polyp segmentation in colonoscopy images. IEEE Access 7:26440–26447. https://doi.org/10.1109/ACCESS.2019.2900672

    Article  Google Scholar 

  32. Costa YMG, Oliveira LS, Silla CN (2017) An evaluation of convolutional neural networks for music classification using spectrograms. Appl Soft Comput J 52:28–38. https://doi.org/10.1016/j.asoc.2016.12.024

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

    Fatih Ertam, Omer Faruk Yakut & Turker Tuncer

Authors
  1. Fatih Ertam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Omer Faruk Yakut
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Turker Tuncer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fatih Ertam.

Ethics declarations

Conflict of interest

The authors confirm that this article content has no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ertam, F., Yakut, O.F. & Tuncer, T. Pattern lock screen detection method based on lightweight deep feature extraction. Neural Comput & Applic 35, 1549–1567 (2023). https://doi.org/10.1007/s00521-022-07846-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07846-6

Keywords

Search

Navigation