Enhancing machine learning robustness against adversarial attacks through cryptographic techniques
*V. Dankan GowdaCorresponding authordankan.v@bmsit.inDepartment of Electronics and Communication EngineeringBMS Institute of Technology and ManagementBangalore, Karnataka, 560064, IndiaView full profile → , Pullela SVVSR Kumarpullelark@yahoo.comDepartment of Computer Science & EngineeringAditya UniversitySurampalem, Andhra Pradesh, 533437, IndiaView full profile → , Praveen Damacharlapraveen@kineticai.comDepartment of Research and DevelopmentKineticAI IncThe Woodlands, TX, 77380, U.S.A.View full profile → , Manoj Tarambalemanoj.tarambale@gmail.comDepartment of Electrical EngineeringPVG’S College Of Engineering and Technology & G. K. Pate (Wani) Institute of ManagementPune, Maharashtra, 411009, IndiaView full profile → , Prasanna Kumar Lakinenilpk.lakineni@gmail.comDepartment of Computer Science and EngineeringKoneru Lakshmaiah Education FoundationVaddeswaram, Andhra Pradesh, 522502, IndiaView full profile → , Kalavakolanu Sripathik.sripathi@sibmhyd.edu.inSymbiosis International (Deemed University) HyderabadSymbiosis Institute of Business ManagementPune, Hyderabad, 509217, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 02 Oct 2024
- Published Online:
- 31 May 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1818
- Pages:
- 927–937
Abstract
Keywords
Subject Classifications
References
[1] T. Zhang, Y. Liu, and X. Wang, “Adversarial attack and defense in deep learning: A survey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 11, pp. 3724–3741 (Nov. 2020).
[2] J. Goodfellow, I. J. Mironov, and C. Song, “Robustness of machine learning models against adversarial attacks,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1555–1567 (Dec. 2020).
[3] S. M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “DeepFool: A simple and accurate method to fool deep neural networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2574–2580 (Dec. 2020).
[4] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770–778 (2019).
[5] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in Proceedings of the International Conference on Learning Representations (ICLR), Banff, Canada (2019).
[6] R. S. Kumar and K. D. V. Prasad, “Securing digital authentication with cryptographic innovations in intrinsic verification systems,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-A, pp. 317–328 (2024).
[7] K. D. V. Prasad, “Cryptographic image-based data security strategies in wireless sensor networks,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-A, pp. 293–304 (2024).
[8] P. Sarma and M. Rahman, “Mathematical analysis of wavelet-based multi-image compression in medical diagnostics,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-B, pp. 675–687 (2024).
[9] A. Kumar, “A novel approach of unsupervised feature selection using iterative shrinking and expansion algorithm,” Journal of Interdisciplinary Mathematics, vol. 26, no. 3, pp. 519–530 (2023).
[10] A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada (2019).
[11] J. Su, D. V. Vargas, and K. Sakurai, “One pixel attack for fooling deep neural networks,” IEEE Transactions on Evolutionary Computation, vol. 23, no. 5, pp. 828–841 (Oct. 2019).
[12] B. Biggio and F. Roli, “Wild patterns: Ten years after the rise of adversarial machine learning,” Pattern Recognition, vol. 84, pp. 317–331 (Dec. 2018).
[13] N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Proceedings of the IEEE Symposium on Security and Privacy (S&P), San Jose, CA, USA, pp. 39–57 (2018).
[14] G. Manivasagam and K. D. V. Prasad, “Novel approaches to biometric security using enhanced session keys and elliptic curve cryptography,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-A, pp. 477–488 (2024).
[15] V. Sivakumar, “A novel RF-SMOTE model to enhance the definite apprehensions for IoT security attacks,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 3, pp. 861–873 (2023).
[16] A. Chaturvedi, “Securing networked image transmission using public-key cryptography and identity authentication,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 3, pp. 779–791 (2023).
[17] K. Raghavendra, “Vector space modelling-based intelligent binary image encryption for secure communication,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 25, no. 4, pp. 1157–1171 (2022).
[18] K. Grosse, N. Papernot, P. Manoharan, M. Backes, and P. McDaniel, “Adversarial perturbations against deep neural networks for malware classification,” in Proceedings of the European Symposium on Research in Computer Security (ESORICS), Oslo, Norway, pp. 62–79 (2017).
[19] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149 (Jun. 2017).
[20] Y. Liu, X. Chen, C. Liu, and D. Song, “Delving into transferable adversarial examples and black-box attacks,” in Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada (2019).
