The Vulnerability of the Neural Networks Against Adversarial Examples in Deep Learning Algorithms

TL;DR

This paper reviews the security vulnerabilities of deep learning models to adversarial examples, analyzing attack and defense methods in black and white box scenarios, and discusses future research directions.

Contribution

It provides a comprehensive classification and comparison of existing attack and defense techniques against adversarial examples in deep learning.

Findings

White box attacks are detailed and compared with black box attacks.

Defense methods are analyzed for their effectiveness against different attack types.

The paper highlights current challenges and future prospects in adversarial robustness.

Abstract

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.