Adversarial Machine Learning for Secure Artificial Intelligence: Challenges, Defense Techniques and Future Directions

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]