Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack
<|reference_start|>Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack: 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.<|reference_end|>
@article{li2024signal,
    title={Signal Adversarial Examples Generation for Signal Detection Network via
  White-Box Attack},
    author={Dongyang Li, Linyuan Wang, Guangwei Xiong, Bin Yan, Dekui Ma, Jinxian
  Peng},
    journal={arXiv preprint arXiv:2410.01393},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01393},
    primaryClass={cs.CV cs.CR}
}