Ketahanan Pembelajaran Mesin terhadap Adversarial examples: Metodologi dan Pertahanan - Ade Kurniawan; Ely Aprilia; Achmad Indra Aulia; Amril Mutoi Siregar; Leonard Goeirmanto (Faktor Exacta, 2025)
This paper examines the vulnerability of machine learning models to adversarial examples: inputs that are subtly manipulated to deceive a model into making incorrect predictions. Although deep learning has demonstrated remarkable performance across various tasks, the security of these models remains a significant challenge. This study provides a comprehensive review of various methods for generating adversarial examples, a classification of attack techniques, and corresponding defense strategies, including both active and passive approaches. The findings indicate that a combination of several defense techniques is significantly more effective in enhancing model robustness compared to any single approach. This research is expected to provide a foundation for the development of more secure and reliable machine learning models for critical applications.