Improvement of the robustness of deep learning models against adversarial attacks

Artificial intelligence has had significant and widespread advancements globally in recent years, primarily due to the exceptional progress of deep learning, a subset of machine learning. Currently, there is a rapid and significant rise in the adoption of deep learning as a research tool across several scientific disciplines worldwide. It has emerged as an integrative and versatile method for conducting general scientific research. Nevertheless, the extensive prevalence and ongoing refinement of adversarial assaults have become an intrinsic drawback of deep learning models, compromising the model's stability. Hence, enhancing the resilience of deep learning models against adversarial attacks has emerged as a highly focused area of research. This study employs the research method of literature analysis and literature review to examine the resilience and vulnerability of deep learning. It explores several approaches to enhance the robustness of deep learning models and provides a comprehensive summary and insights into improving these models.