Adversarial signal detection method based on multi-channel feature reconstruction

Abstract

The invention discloses an adversarial signal detection method based on multichannel feature reconstruction, and the method comprises the steps: firstly collecting a signal data set, inputting the signal data set into a feature extraction depth receiver, calculating an embedded feature map and a corresponding semantic cluster, and inputting the embedded feature map and the corresponding semantic cluster into a multichannel feature encoder; the multi-channel feature encoder comprises a private semantic encoder, a public semantic encoder and a noise encoder; training the encoder, and reconstructing the encoder to obtain reconstructed features; and inputting the reconstructed features into a meta-classifier, training the meta-classifier, and completing distinguishing of normal samples and adversarial samples. According to the method, the early shooting features are extracted through the noise feature encoder, multi-feature reconstruction is carried out on the signal features, and the difference between an adversarial sample and a normal sample is enhanced. According to the adversarial signal detection device based on multichannel feature reconstruction, noise features of adversarial samples are extracted, so that smaller adversarial disturbance can be detected more accurately.

Description

technical field [0001] This patent relates to the security field of artificial intelligence and wireless communication, and specifically relates to a countermeasure signal detection method based on multi-channel feature reconstruction. Background technique [0002] The wireless communication system is composed of a transmitting end and a receiving end. The transmitting end performs channel coding, modulation and pulse shaping on the signal before sending. The receiving end of a traditional wireless communication system uses processes such as carrier and symbol synchronization, channel estimation, equalization, demodulation, and decoding to recover information from the received distorted signal. This step-by-step signal processing method at the receiving end has the problem of error accumulation. , and each processing module is based on theoretical assumptions, in the actual signal recovery scenario, there is a problem of poor signal recovery effect. [0003] The neural netw...

AI Technical Summary

Problems solved by technology

[0004] Illegal attackers send micro-power anti-interference superimposed on the normal signal of the transmitter through the propagation channel. Such anti-signals will increase the bit error rate of the depth receiver, and are dif

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