Adversarial AI: Threats, Defenses, and the Role of Explainability in Building Trustworthy Systems - Deepak Kejriwal; Pujari, Tejaskumar Dattatray (Asosiasi Dosen Muda Indonesia, 2023)
Artificial Intelligence has made possible the latest revolutions in the industry. Nevertheless, adversarial AI turns out to be a serious challenge because of its tendency to exploit the vulnerabilities of machine learning models, breach their security, and eventually lead them to fail, mostly unless very few. Adversarial attacks can be evasion and poisoning, model inversion, and so forth; they indeed say how fragile an AI system is and also suggest a proper immediate call for solid defensive structures. Several adversarial defense mechanisms have been proposed―from adversarial training to defensive distillation and certified defenses―yet they remain vulnerable to high-level attacks. This included the emergence of explainable artificial intelligence (XAI) as one of the significant components in AI security, whereby capturing interpretability and transparency can lead to better threat detection and user trust. This work encompasses a literature review of adversarial AIs, current developments in adversarial defenses, and the role played by XAI in reducing threats from such adversarial systems. In effect, the paper presents an integrated framework with techniques of explainability for the building of resilient, transparent, and trustworthy AI systems.