⚠ Official Notice: www.ijisrt.com is the official website of the International Journal of Innovative Science and Research Technology (IJISRT) Journal for research paper submission and publication. Please beware of fake or duplicate websites using the IJISRT name.



Adversarial Machine Learning: Attacks, Defenses, and the Path Towards Trustworthy AI


Authors : Jieyao Pang

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/7z8p4uk2

Scribd : https://tinyurl.com/5a2dah9y

DOI : https://doi.org/10.38124/ijisrt/26jun1631

PlumX Metrics

Semantic Scholar

ResearchGate

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Deep neural networks achieve strong performance on perception tasks but remain vulnerable to adversarial examples—imperceptibly perturbed inputs that induce confident misclassification. This dissertation reviews the adversarial attack–defence landscape and reports CIFAR-10 experiments using ResNet-18. It compares a standard baseline, a PGDadversarially trained model, and a model obtained from RobustBench under FGSM, PGD-20, and AutoAttack.

Keywords : Adversarial Machine Learning, Adversarial Examples, Adversarial Training, PGD, FGSM, AutoAttack, RobustBench, CIFAR-10, Trustworthy AI.

References :

  1. C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv:1312.6199, 2013. [Online]. Available: https://arxiv.org/abs/1312.6199
  2. I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv:1412.6572, 2014. [Online]. Available: https://arxiv.org/abs/1412.6572
  3. A. Athalye, N. Carlini, and D. Wagner, “Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,” in Proc. 35th Int. Conf. Mach. Learn. (ICML), vol. 80, 2018, pp. 274–283
  4. F. Croce and M. Hein, “Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,” arXiv:2003.01690, 2020. [Online]. Available: https://arxiv.org/abs/2003.01690
  5. N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” arXiv:1608.04644, 2016. [Online]. Available: https://arxiv.org/abs/1608.04644
  6. S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “DeepFool: A simple and accurate method to fool deep neural networks,” arXiv:1511.04599, 2015. [Online]. Available: https://arxiv.org/abs/1511.04599
  7. H. Zhang, Y. Yu, J. Jiao, E. P. Xing, L. El Ghaoui, and M. I. Jordan, “Theoretically principled trade-off between robustness and accuracy,” arXiv:1901.08573, 2019. [Online]. Available: https://arxiv.org/abs/1901.08573
  8. L. Rice, E. Wong, and J. Z. Kolter, “Overfitting in adversarially robust deep learning,” arXiv:2002.11569, 2020. [Online]. Available: https://arxiv.org/abs/2002.11569
  9. J. M. Cohen, E. Rosenfeld, and J. Z. Kolter, “Certified adversarial robustness via randomized smoothing,” arXiv:1902.02918, 2019. [Online]. Available: https://arxiv.org/abs/1902.02918
  10. Y. Wu and K. He, “Group normalization,” in Proc. Eur. Conf. Comput. Vis. (ECCV), 2018, pp. 3–19. https://arxiv.org/abs/1803.08494
  11. L. Rice, E. Wong, and J. Z. Kolter, “Overfitting in adversarially robust deep learning,” arXiv:2002.11569, 2020. [Online]. Available: https://arxiv.org/abs/2002.11569
  12. A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in Proc. Int. Conf. Learn. Representations (ICLR), 2018.
  13. K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song, “Robust physical-world attacks on deep learning visual classification,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2018, pp. 1625–1634, doi: 10.1109/CVPR.2018.00175.
  14. National Institute of Standards and Technology, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, NIST AI 100-2e2023, 2024. [Online]. Available: https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2023.pdf
  15. K. Hu, A. Shih, S. Yousif, S. Wang, P. Zizka, M. A. Bautista, J. S. Bhatia, M. Miller, X. Ju, M. Korablyov, M. Reimer, et al., “Scaling in depth: Unlocking robustness certification on ImageNet,” arXiv:2301.12549, 2023. [Online]. Available: https://arxiv.org/abs/2301.12549
  16. R. Duan, Y. Dong, J. Bao, Q. Zhang, Q. Tian, C. Xu, and H. Su, “Adversarial laser beam: Effective physical-world attack to DNNs in a blink,” arXiv:2103.06504, 2021. [Online]. Available: https://arxiv.org/abs/2103.06504
  17. E. Shayegani, M. A. A. Mamun, Y. Fu, P. Zaree, Y. Dong, and N. Abu-Ghazaleh, “Survey of vulnerabilities in large language models revealed by adversarial attacks,” arXiv:2310.10844, 2023. [Online]. Available: https://arxiv.org/abs/2310.10844
  18. M. Zhao, L. Zhang, J. Ye, H. Lu, B. Yin, and X. Wang, “Adversarial training: A survey,” arXiv:2410.15042, 2024. [Online]. Available: https://arxiv.org/abs/2410.15042

Deep neural networks achieve strong performance on perception tasks but remain vulnerable to adversarial examples—imperceptibly perturbed inputs that induce confident misclassification. This dissertation reviews the adversarial attack–defence landscape and reports CIFAR-10 experiments using ResNet-18. It compares a standard baseline, a PGDadversarially trained model, and a model obtained from RobustBench under FGSM, PGD-20, and AutoAttack.

Keywords : Adversarial Machine Learning, Adversarial Examples, Adversarial Training, PGD, FGSM, AutoAttack, RobustBench, CIFAR-10, Trustworthy AI.

Paper Submission Last Date
31 - July - 2026

SUBMIT YOUR PAPER CALL FOR PAPERS
Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe