Year: 2025
Type: article
Abstract: 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.
Source: Electronics Letters
DOI: 10.1049/ell2.70348
Authors: Dongyang Li, Linyuan Wang, Guangwei Xiong, Jinxian Peng, Dekui Ma, Bin Yan
Institution: Chinese People's Liberation Army
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,590
Award: G8094866363
OpenAlex ID: W4412431882