Quantum fuzzy machine learning adversarial defense model method

Abstract

The invention discloses a quantum fuzzy machine learning adversarial defense model method. The method comprises the steps of S1, constructing a quantum fuzzy data sample of a legal user; S2, simulating a malicious attacker to construct an attack strategy: adding the constructed disturbance into a quantum fuzzy data sample of a legal user to form a quantum fuzzy countermeasure sample of the malicious attacker; S3, submitting the quantum fuzzy data sample of the legal user and the quantum fuzzy adversarial sample of the malicious attacker to a quantum fuzzy machine learning system for training and learning, and making a correct decision by the quantum fuzzy machine learning system, wherein the quantum fuzzy machine learning system comprises an adversarial defense module, and the adversarialdefense module is an adversarial sample for defending malicious attackers, so that the quantum fuzzy machine learning system makes a correct decision. The model method can effectively resist the attack of a malicious attacker, improve the safety and robustness of a quantum fuzzy machine learning system, and ensure the safe and reliable operation of a quantum fuzzy machine learning algorithm.

Description

technical field [0001] The invention relates to the fields of quantum machine learning, fuzzy set theory and network confrontation, in particular to a quantum fuzzy machine learning confrontation defense model. Background technique [0002] In recent years, many research results have been achieved in the field of machine learning, and successful applications have been achieved in many fields, but machine learning also faces many security risks. For example, machine learning systems are easily fooled by adversarial examples, resulting in wrong classifications; users who use online machine learning systems for classification have to disclose their data to the server, which will lead to privacy leaks, and what is worse is the widespread use of machine learning. Use is exacerbating these security risks. At present, many researchers are exploring and studying potential attacks on deep learning and corresponding defense techniques. In the past three years, research on machine le...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the above problems, the object of the present invention is to provide a quantum fuzzy machine learning confrontation defense model method, which can effectively resist the attacks of malicious attackers, improve the security and robustness of the quantum fuzzy machine learning system, and ensure the quantum fuzzy machine learning system. The fuzzy machine learning algorithm operates safely and reliably, and at the same time, it can efficiently, safely and accurately deal with the complexity and uncertainty of big data and enrich the theory, technology and research methods of "quantum computing + artificial intelligence"

Images