Adversarial examples detection based on quantum fuzzy convolution neural network

Combining the advantages of quantum computing and machine learning, quantum machine learning is anticipated to advance the field of artificial intelligence further. Recent research has demonstrated, however, that the state-of-the-art quantum classifiers can be deceived by adversarial examples, leading to incorrect classifications by quantum models. This paper proposes the quantum fuzzy convolutional neural network, which is used to detect adversarial examples, by combining the benefits of fuzzy systems and the quantum convolutional neural network. This defensive strategy does not modify the structure and training process of the quantum classifier that requires protection. Simulation experiments show that the adversarial example detection approach proposed in this paper can successfully separate the real data distribution from the adversarial data distribution and detect the adversarial examples generated by a specific attack method on a specific quantum classifier.