This master’s report aims to provide a comprehensive review of the literature on the robustness of machine learning models against adversarial attacks. The pri-mary objectives are to explore existing methodologies, highlight key research find-ings, and identify gaps in current knowledge. The report examines autoencoder-based approaches for detecting adversarial examples as well as other defensive techniques such as adversarial training and regularization techniques. Various adversarial crafting methods, such as Fast Gradient Sign Method (FGSM)[10] and Projected Gradient Descent (PGD)[17], are analyzed in depth. The insights gained will serve as a solid foundation for the development of more robust models in future research.