Adversarial Training for Mitigating Insider-Driven XAI-Based Backdoor Attacks

R. G. Gayathri (), Atul Sajjanhar () and Yong Xiang
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R. G. Gayathri: School of Information Technology, Deakin University, Geelong, VIC 3217, Australia
Atul Sajjanhar: School of Information Technology, Deakin University, Geelong, VIC 3217, Australia
Yong Xiang: School of Information Technology, Deakin University, Geelong, VIC 3217, Australia

Future Internet, 2025, vol. 17, issue 5, 1-21

Abstract: The study investigates how adversarial training techniques can be used to introduce backdoors into deep learning models by an insider with privileged access to training data. The research demonstrates an insider-driven poison-label backdoor approach in which triggers are introduced into the training dataset. These triggers misclassify poisoned inputs while maintaining standard classification on clean data. An adversary can improve the stealth and effectiveness of such attacks by utilizing XAI techniques, which makes the detection of such attacks more difficult. The study uses publicly available datasets to evaluate the robustness of the deep learning models in this situation. Our experiments show that adversarial training considerably reduces backdoor attacks. These results are verified using various performance metrics, revealing model vulnerabilities and possible countermeasures. The findings demonstrate the importance of robust training techniques and effective adversarial defenses to improve the security of deep learning models against insider-driven backdoor attacks.

Keywords: adversarial training; backdoor attacks; data poisoning; insider threat; generative models; explainable AI (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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