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

TL;DR

This paper introduces a white-box attack method for generating adversarial examples in signal detection networks, significantly reducing their performance by carefully adding perturbations constrained by energy ratios.

Contribution

The paper proposes a novel gradient-based adversarial attack method for signal detection networks using L2-norm constraints in time and frequency domains.

Findings

28.1% reduction in mean Average Precision

24.7% reduction in recall

30.4% reduction in precision

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 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 28.1% reduction in the mean Average Precision (mAP), a 24.7% reduction in recall, and a 30.4% reduction in precision of the signal detection network. Compared to random noise perturbation of equivalent intensity, our adversarial attack demonstrates a significant attack effect.