  With further development in the fields of computer vision, network security,
natural language processing and so on so forth, deep learning technology
gradually exposed certain security risks. The existing deep learning algorithms
cannot effectively describe the essential characteristics of data, making the
algorithm unable to give the correct result in the face of malicious input.
Based on current security threats faced by deep learning, this paper introduces
the problem of adversarial examples in deep learning, sorts out the existing
attack and defense methods of the black box and white box, and classifies them.
It briefly describes the application of some adversarial examples in different
scenarios in recent years, compares several defense technologies of adversarial
examples, and finally summarizes the problems in this research field and
prospects for its future development. This paper introduces the common white
box attack methods in detail, and further compares the similarities and
differences between the attack of the black and white box. Correspondingly, the
author also introduces the defense methods, and analyzes the performance of
these methods against the black and white box attack.
