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Bridging Digital Deception and Physical Fidelity: A Spatio-Temporal Attack on Cognitive Radars

Tongrun Xing, Yongjun Dai, Xuan Liao, Zhiwen Zhang, Tong Zhou, Longlong Zhang, Tian Jiang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27585 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

Sin embargo, los ataques existentes se enfrentan a dos limitaciones en la guerra electrónica práctica: baja transferibilidad de arquitectura transversal y severa vulnerabilidad a las distorsiones de canales físicos, lo que degrada su fiabilidad táctica. Para superar estas barreras de botella, proponemos un Attaque Segmentado Espacial-Temporal (SSTA) físico pre-dirixido. En primer lugar, un mecanismo de segmentación global-local que desacopla el mecanismo de segmentación interrumpe la dependencia de los gradientes de los canales adversarios en la coherencia de la secuencia de largo alcance (0), poniendo puente a la brecha de características de la arquitectura transversal. En segundo lugar, más allá de las restricciones estáticas de seguridad sin semántica física, confirmamos las distorsiones

Machine translation (Meta M2M100); the English original follows.

Abstract

As cognitive radars evolve towards the end-to-end processing of one-dimensional complex baseband sequences, deep networks face severe adversarial threats. However, existing attacks encounter dual limitations in practical electronic warfare: low cross-architecture transferability and severe vulnerability to physical channel distortions, which degrades their tactical reliability. To overcome these bottlenecks, we propose a physical prior-driven Segmented Spatio-Temporal Attack (SSTA). First, a global-local feature decoupling segmentation mechanism interrupts the adversarial gradients' dependence on long-range sequence coherence, bridging the cross-architecture feature gap. Second, moving beyond static norm constraints devoid of physical semantics, we abstract channel distortions into differentiable spatio-temporal operators, integrating them into momentum iterations via an Expectation over Transformation (EoT) strategy. Comprehensive cross-evaluations across 9 heterogeneous models spanning CNNs, RNNs, and Transformers demonstrate that SSTA outperforms the state-of-the-art baseline by 10.36% in Average Black-Box Attack Success Rate. Furthermore, baseband equivalent simulations confirm that the generated waveforms survive extreme physical distortions and, after matched filtering, achieve zero main-lobe attenuation (0 dB) with noise floor lift strictly within Constant False Alarm Rate tolerances. SSTA successfully unifies digital-domain deception and physical-domain fidelity, providing crucial support for CEW security assessment.

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Topics: Computer science, Bridging (networking), Deception, Constant false alarm rate, Scalability, False alarm

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