Evaluating the Vulnerabilities in ML systems in terms of adversarial
  attacks
John Harshith Mantej Singh Gill Madhan Jothimani
There have been recent adversarial attacks that are difficult to find. These
new adversarial attacks methods may pose challenges to current deep learning
cyber defense systems and could influence the future defense of cyberattacks.
The authors focus on this domain in this research paper. They explore the
consequences of vulnerabilities in AI systems. This includes discussing how
they might arise, differences between randomized and adversarial examples and
also potential ethical implications of vulnerabilities. Moreover, it is
important to train the AI systems appropriately when they are in testing phase
and getting them ready for broader use.