Black box attack method for brain-computer interface system

Abstract

The invention discloses a black box attack method for a brain-computer interface system. Firstly, generating EEG samples with more information amount and diversity based on inquiry synthesis active learning to substitute model training samples, then training an alternative model based on the training sample, so that the alternative model can better approximate a target model, and finally, generating an adversarial sample on the trained alternative model, and performing black box attack on a brain-computer interface system of the EEG by using the adversarial sample, so that the brain-computer interface system can be wrongly classified on the target model. Compared with a traditional black box attack method based on a Jacobian matrix, the method has a better attack effect. The same or betterattack performance can be obtained under the condition of fewer inquiry times, the generated countermeasure sample has very low noise, is almost not different from the original EEG signal in time domain and frequency domain and cannot be easily found, and the attack efficiency of the black box attack in the brain-computer interface system is greatly improved.

Description

technical field [0001] The invention belongs to the security field of an EEG-based brain-computer interface system, and more specifically relates to a black-box attack method for a brain-computer interface system. Background technique [0002] The brain-computer interface system is a real-time communication system that connects the brain and external electronic devices. The system directly collects the physiological electrical signals generated by the human brain, and then converts them into commands that can control external electronic devices, thereby replacing human natural limbs. Or language organ, communicate with the outside world, control the external environment. EEG is by far the most widely used input signal for BCI systems due to its ease of use, relatively low cost, and minimal risk to the user. In the brain-computer interface system, the machine learning module is the most important module. With the development of deep learning in recent years, some deep learni...

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Problems solved by technology

[0006] Aiming at the defects of the prior art, the purpose of the present invention is to propose a black-box attack method of the brain-computer interface system, which aims to solve the attack efficiency caused by the need for a large number of inquiries to collect enough information when training the replacement model in the prior art lower question

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