Adversarial Machine Learning for Secure Artificial Intelligence: Challenges, Defense Techniques and Future Directions
Author(s): Joga Ram Kumawat
Publication #: 2609024
Date of Publication: 19.09.2026
Country: India
Pages: 1-7
Published In: Volume 12 Issue 5 September-2026
DOI: https://doi.org/10.62970/IJIRCT.v12.i5.2609024
Abstract
Adversarial machine learning studies how attackers trick artificial intelligence (AI) models and how we can build safer systems using multi-layered defenses. [1, 2]. As detailed in research on Adversarial Machine Learning and Secure Artificial Intelligence Systems, protecting AI requires continuous care across its entire lifecycle. [1]
Evasion Attacks: Hackers change input data slightly during testing to make the AI make wrong choices. [1]
Data Poisoning: Bad actors inject fake data into the training set to ruin the model.
Backdoor Insertion: Attackers hide secret triggers inside a model that only activate under specific conditions. [1]
Privacy Leaks: Thieves use model outputs to steal private training data or copy the model itself. [1, 2]
Keywords: adversarial, AI, defense, machine learning, future
Download/View Count: 4
Share this Article