T-ADD: Enhancing DOA Estimation Robustness Against Adversarial Attacks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911313156898816 |
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| author | Zheng, Shilian Wu, Xiaoxiang Zhang, Luxin Yue, Keqiang Qi, Peihan Zhao, Zhijin |
| author_facet | Zheng, Shilian Wu, Xiaoxiang Zhang, Luxin Yue, Keqiang Qi, Peihan Zhao, Zhijin |
| contents | Deep learning has achieved remarkable success in direction-of-arrival (DOA) estimation. However, recent studies have shown that adversarial perturbations can severely compromise the performance of such models. To address this vulnerability, we propose Transformer-based Adversarial Defense for DOA estimation (T-ADD), a transformer-based defense method designed to counter adversarial attacks. To achieve a balance between robustness and estimation accuracy, we formulate the adversarial defense as a joint reconstruction task and introduce a tailored joint loss function. Experimental results demonstrate that, compared with three state-of-the-art adversarial defense methods, the proposed T-ADD significantly mitigates the adverse effects of widely used adversarial attacks, leading to notable improvements in the adversarial robustness of the DOA model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | T-ADD: Enhancing DOA Estimation Robustness Against Adversarial Attacks Zheng, Shilian Wu, Xiaoxiang Zhang, Luxin Yue, Keqiang Qi, Peihan Zhao, Zhijin Signal Processing Deep learning has achieved remarkable success in direction-of-arrival (DOA) estimation. However, recent studies have shown that adversarial perturbations can severely compromise the performance of such models. To address this vulnerability, we propose Transformer-based Adversarial Defense for DOA estimation (T-ADD), a transformer-based defense method designed to counter adversarial attacks. To achieve a balance between robustness and estimation accuracy, we formulate the adversarial defense as a joint reconstruction task and introduce a tailored joint loss function. Experimental results demonstrate that, compared with three state-of-the-art adversarial defense methods, the proposed T-ADD significantly mitigates the adverse effects of widely used adversarial attacks, leading to notable improvements in the adversarial robustness of the DOA model. |
| title | T-ADD: Enhancing DOA Estimation Robustness Against Adversarial Attacks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2512.10496 |