Title: Adversarial Examples: Opportunities and Challenges,
Abstract: With the advent of the era of artificial intelligence(AI), deep neural
networks (DNNs) have shown huge superiority over human in image recognition,
speech processing, autonomous vehicles and medical diagnosis. However, recent
studies indicate that DNNs are vulnerable to adversarial examples (AEs) which
are designed by attackers to fool deep learning models. Different from real
examples, AEs can hardly be distinguished from human eyes, but mislead the
model to predict incorrect outputs and therefore threaten security critical
deep-learning applications. In recent years, the generation and defense of AEs
have become a research hotspot in the field of AI security. This article
reviews the latest research progress of AEs. First, we introduce the concept,
cause, characteristic and evaluation metrics of AEs, then give a survey on the
state-of-the-art AE generation methods with the discussion of advantages and
disadvantages. After that we review the existing defenses and discuss their
limitations. Finally, the future research opportunities and challenges of AEs
are prospected.