Keywords Adversarial $\cdot$ Security $\cdot$ Deep Learning.

1 Introduction

2 Backgrounds  
2.1 The "Imperceivable" Perturbation  
2.2 Measuring the Perturbation  
2.3 Non-deep-learning Adversarial  
2.4 Derived Topics  

3 Attacking Strategies  
3.1 A Taxonomy of Attacks  
3.2 White-box Attacks  
3.3 Black-box Attacks  

4 Defending Strategies  
4.1 Intuitions  
4.2 Strengthening the Training Process  
4.3 Test Time Perturbation Recovery  
4.4 The Fragility of Defenses  

5 Extended Studies  
5.1 Verifying the Robustness  
5.2 Adversarial in Reinforcement Learning  
5.3 Attacks in Physical World  

6 Conclusions