A Survey of Robustness and Safety of 2D and 3D Deep Learning Models
  Against Adversarial Attacks
Yanjie Li Bin Xie Songtao Guo Yuanyuan Yang Bin Xiao
Benefiting from the rapid development of deep learning, 2D and 3D computer
vision applications are deployed in many safe-critical systems, such as
autopilot and identity authentication. However, deep learning models are not
trustworthy enough because of their limited robustness against adversarial
attacks. The physically realizable adversarial attacks further pose fatal
threats to the application and human safety. Lots of papers have emerged to
investigate the robustness and safety of deep learning models against
adversarial attacks. To lead to trustworthy AI, we first construct a general
threat model from different perspectives and then comprehensively review the
latest progress of both 2D and 3D adversarial attacks. We extend the concept of
adversarial examples beyond imperceptive perturbations and collate over 170
papers to give an overview of deep learning model robustness against various
adversarial attacks. To the best of our knowledge, we are the first to
systematically investigate adversarial attacks for 3D models, a flourishing
field applied to many real-world applications. In addition, we examine physical
adversarial attacks that lead to safety violations. Last but not least, we
summarize present popular topics, give insights on challenges, and shed light
on future research on trustworthy AI.