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arXiv Machine Learning By Tongrun Xing, Yongjun Dai, Xuan Liao, Zhiwen Zhang, Tong Zhou, Longlong Zhang, Tian Jiang

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

Read the original on arXiv Machine Learning →

The paper introduces the Segmented Spatio-Temporal Attack (SSTA), a new adversarial method for cognitive radars that addresses two key limitations of existing attacks: low cross-architecture transferability and sensitivity to physical channel distortions. SSTA uses a global‑local feature decoupling segmentation to break the dependence on long‑range sequence coherence, and incorporates differentiable spatio‑temporal operators into an Expectation over Transformation strategy to model physical distortions. Experiments on nine diverse models (CNNs, RNNs, Transformers) show a 10.36% improvement in average black‑box attack success rate, and simulations confirm that the crafted waveforms survive extreme distortions while maintaining acceptable signal quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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