Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack

Abstract

With the development and application of deep learning in signal detection tasks, the vulnerability of neural networks to adversarial attacks has also become a security threat to signal detection networks. This paper defines a signal adversarial examples generation model for signal detection network from the perspective of adding perturbations to the signal. The model uses the inequality relationship of L2-norm between time domain and time-frequency domain to constrain the energy of signal perturbations. Building upon this model, we propose a method for generating signal adversarial examples utilizing gradient-based attacks and inverse short-time Fourier transform. The experimental results show that under the constraint of signal perturbation energy ratio less than 3$\%$, our adversarial attack resulted in a 34.5$\%$ reduction in the mean average precision (mAP), a 31.6$\%$ reduction in recall, and a 37.5$\%$ reduction in precision of the signal detection network. Compared to random noise perturbation of equivalent intensity, our adversarial attack demonstrates a significant attack effect.