Adversarial Vulnerability of Deep Learning–Based LPI Detectors 


Vol. 51,  No. 2, pp. 475-484, Feb.  2026
10.7840/kics.2026.51.2.475


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  Abstract

Deep learning detectors operating on time-frequency images (TFIs) achieve strong performance in detecting and recognizing low-probability-of-intercept (LPI) radar waveforms, yet their robustness to adversarial perturbations has received limited attention. This paper provides a modulation-wise evaluation of two canonical attacks—Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD)—against a convolutional neural networks based LPI detector under a black-box threat model, where transferable perturbations are crafted via surrogate models. Experiments cover twelve representative LPI modulations. Using per-modulation adversarial accuracy, attack success rate, and a TFI-domain perturbation budget , we demonstrate both the effectiveness of adversarial examples and quantify the distortion–evasion trade-off relative to the original signals. The findings offer modulation-aware guidance on optimal attack settings, and highlight configurations that achieve maximal evasion with minimal perturbation.

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[IEEE Style]

M. Lee, S. Park, S. Cho, Y. W. Law, S. Lee, Y. Kim, "Adversarial Vulnerability of Deep Learning–Based LPI Detectors," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 2, pp. 475-484, 2026. DOI: 10.7840/kics.2026.51.2.475.

[ACM Style]

Moohyun Lee, Sungjae Park, Sunghwan Cho, Yee Wei Law, Sang-Heon Lee, and Yongchul Kim. 2026. Adversarial Vulnerability of Deep Learning–Based LPI Detectors. The Journal of Korean Institute of Communications and Information Sciences, 51, 2, (2026), 475-484. DOI: 10.7840/kics.2026.51.2.475.

[KICS Style]

Moohyun Lee, Sungjae Park, Sunghwan Cho, Yee Wei Law, Sang-Heon Lee, Yongchul Kim, "Adversarial Vulnerability of Deep Learning–Based LPI Detectors," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 2, pp. 475-484, 2. 2026. (https://doi.org/10.7840/kics.2026.51.2.475)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880