A Systematic Review of Adversarial Machine Learning and Deep Learning Applications

Abstract

The review delves into creating an understandable framework for machine learning in robotics. It stresses the significance of machine learning in materials science and robotics highlighting how it can transform industries by boosting efficiency and deepening our knowledge of materials on levels. The review also discusses the hurdles posed by attacks on machine learning and the increasing relevance of machine learning in software development. It outlines the approach used in the review, including the search strategy criteria for inclusion and exclusion and the process for selecting studies, including adherence to research published in English only. The classification section organizes the chosen studies into six areas: reinforcement learning, adversarial techniques, applications of learning, and image recognition. In the Discussion section, challenges like critical learning models in robotics unsupervised learning, adversarial attacks on datasets, and limited data for polyp detection are identified. Recommendations for research are provided along with insights into motivations behind these studies; topics covered include reinforcement learning, adversarial examples, domain alignment, and world adversarial attacks on industrial systems.

Keywords

• Robotics, Adversarial Attack, Security, Artificial Intelligence, Machine Learning, Deep Learning