Ketahanan Pembelajaran Mesin terhadap Adversarial examples: Metodologi dan Pertahanan

Kurniawan, Ade (Institut Teknologi Sains Bandung)
Aprilia, Ely (Institut Teknologi Sains Bandung)
Aulia, Achmad Indra (Institut Teknologi Sains Bandung)
Siregar, Amril Mutoi (Institut Teknologi Sains Bandung)
Goeirmanto, Leonard (Institut Teknologi Sains Bandung)

Faktor Exacta
Vol 18, No 2 (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.