Adversarial Vulnerability of Deep Learning–Based LPI Detectors

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.