Quantum fuzzy machine learning countermeasure attack model method

A technology for attacking models and machine learning, applied in machine learning, quantum computers, computing models, etc., to achieve reliable operation, improve security and robustness

Pending Publication Date: 2021-02-19
CHENGDU UNIV OF INFORMATION TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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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 anti-attack model method. This method aims at the vulnerability and insufficiency of the quantum fuzzy machine learning algorithm, and carries out

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  • Quantum fuzzy machine learning countermeasure attack model method
  • Quantum fuzzy machine learning countermeasure attack model method
  • Quantum fuzzy machine learning countermeasure attack model method

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Embodiment 1

[0045] A quantum fuzzy machine learning confrontation attack model method, its flow chart is as follows figure 1 As shown, the details are as follows:

[0046] S1. Constructing a quantum fuzzy data sample of a legitimate user;

[0047] S2. The malicious attacker builds an attack strategy, and adds the constructed disturbance data to the quantum fuzzy data sample of the legitimate user to form a quantum fuzzy confrontation sample;

[0048] Malicious attackers can choose appropriate attack strategies (such as fast gradient sign method, BIM confrontation attack method, Jacobian-based feature map attack (JSMA), DeepFool, C&W attacks, Universal adversarial perturbations, Houdini, Adversarial Transformation Networks, etc.), the present invention The attack strategy employed is the fast gradient sign method, which is computationally efficient against perturbations.

[0049] S3. The quantum fuzzy machine learning system makes predictions based on quantum fuzzy confrontation samples,...

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PUM

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Abstract

The invention relates to the technical field of quantum machine learning, fuzzy set theories and network confrontation, and provides a quantum fuzzy machine learning confrontation attack model method.The method comprises the steps: S1, constructing a quantum fuzzy data sample of a legal user; S2, constructing an attack strategy by a malicious attacker, and adding the constructed disturbance intoa quantum fuzzy data sample of a legal user to form a quantum fuzzy confrontation sample; S3, enabling the quantum fuzzy machine learning system to perform classification according to the quantum fuzzy adversarial samples and make corresponding correct decisions or error decisions; wherein when the quantum fuzzy adversarial sample is a quantum fuzzy data sample of a legal user, a correct decisionis made; when the quantum fuzzy adversarial sample is a sample constructed by a malicious attacker, an error decision is made, the attack purpose is achieved, the defects of vulnerability and many defects of a quantum fuzzy machine learning algorithm are overcome, and safety and robustness are improved.

Description

technical field [0001] The invention relates to the field of quantum machine learning, fuzzy set theory and network confrontation, in particular to a quantum fuzzy machine learning confrontation attack model method. Background technique [0002] Quantum machine learning (QML), which combines quantum computing with traditional machine learning algorithms, is an important emerging field. Its purpose is to accelerate traditional machine learning algorithms and solve the problem of limited machine learning applications. Both quantum computing and machine learning algorithms began in the 20th century; in 1982, Feynman first proposed to build a computer based on the principles of quantum mechanics to simulate quantum systems. Compared with traditional computers, the simulation efficiency of quantum computers has been improved exponentially. Since then, the research of quantum computing has started. [0003] In 1994, Shor proposed the Shor algorithm, which provides exponential sp...

Claims

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Application Information

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IPC IPC(8): G06N10/00G06N20/00
CPCG06N10/00G06N20/00
Inventor 张仕斌黄曦李同侯敏昌燕闫丽丽
Owner CHENGDU UNIV OF INFORMATION TECH
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