Newest papers

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

German

Da kognitive Alarmradare in Richtung End-to-End-Verarbeitung von ein-dimensionalen komplexen Baseband-Sequenzen weiterentwickeln, stehen tiefe Netzwerke jedoch schweren Gegnerbedrohungen ausgesetzt. Bestehende Angriffe treffen jedoch auf doppelte Einschränkungen in der praktischen elektronischen Kriegsführung: niedrige Cross-Architektur-Übertragbarkeit und schwere Anfälligkeit für physikalische Kanalverzerrungen, die ihre taktische Zuverlässigkeit beeinträchtigen. Um diese Flaschenlücken zu überwinden, schlagen wir einen physikalischen vorangetriebenen Segmentierten Raum-Zeitangriff (SSTA) vor. Erstens unterbricht ein global-lokaler Segmentierungsmechanismus, der Hauptfunktionen entkoppelt, die Geg

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange