LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection

arXiv cs.AI Papers
adversarial-robustness object-detection lipschitz-constrained single-shot-detection ssd safety-critical-systems attack-agnostic

Summary

Introduces LipSSD, a Lipschitz-constrained variant of Single Shot MultiBox Detector that improves adversarial robustness in object detection while being attack-agnostic and complementary to adversarial training. Evaluated on Pascal VOC, LARD, and KITTI datasets.

arXiv:2607.06592v1 Announce Type: cross Abstract: Object detectors have many applications in safety-critical systems, but they are known to be sensitive to worst-case perturbations such as adversarial attacks, which limits their applicability in real-world scenarios. Compared with classification, adversarial robustness for object detection has received less attention, and existing methods are often tied to adversarial training, whose performance may not transfer across attacks, perturbation budgets, or architectures. In this work, we introduce Lipschitz-constrained variants of object detection architectures as robust-by-design alternatives to standard detectors. We validate this approach with LipSSD, a Lipschitz-constrained Single Shot MultiBox Detector (SSD), and provide a comprehensive study of its adversarial robustness using multiple white-box adversarial attacks and datasets. We first analyze the accuracyrobustness trade-off induced by Lipschitz constraints and show that it can be controlled through a single training hyperparameter. We then demonstrate that Lipschitzconstrained detectors are complementary to adversarial training: under the same training setup on the Pascal VOC dataset, adversarially trained LipSSD improves mAP@50 on unseen attacks by up to 15 points over classical adversarially trained SSD. Finally, we use more specific safety-critical datasets such as LARD and KITTI, and show that Lipschitz-constrained detectors can improve robustness while largely preserving clean performance. These results suggest that architectural Lipschitz control is a practical and attack-agnostic direction for improving the robustness of object detectors.
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# LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection
Source: [https://arxiv.org/abs/2607.06592](https://arxiv.org/abs/2607.06592)
[View PDF](https://arxiv.org/pdf/2607.06592)

> Abstract:Object detectors have many applications in safety\-critical systems, but they are known to be sensitive to worst\-case perturbations such as adversarial attacks, which limits their applicability in real\-world scenarios\. Compared with classification, adversarial robustness for object detection has received less attention, and existing methods are often tied to adversarial training, whose performance may not transfer across attacks, perturbation budgets, or architectures\. In this work, we introduce Lipschitz\-constrained variants of object detection architectures as robust\-by\-design alternatives to standard detectors\. We validate this approach with LipSSD, a Lipschitz\-constrained Single Shot MultiBox Detector \(SSD\), and provide a comprehensive study of its adversarial robustness using multiple white\-box adversarial attacks and datasets\. We first analyze the accuracyrobustness trade\-off induced by Lipschitz constraints and show that it can be controlled through a single training hyperparameter\. We then demonstrate that Lipschitzconstrained detectors are complementary to adversarial training: under the same training setup on the Pascal VOC dataset, adversarially trained LipSSD improves mAP@50 on unseen attacks by up to 15 points over classical adversarially trained SSD\. Finally, we use more specific safety\-critical datasets such as LARD and KITTI, and show that Lipschitz\-constrained detectors can improve robustness while largely preserving clean performance\. These results suggest that architectural Lipschitz control is a practical and attack\-agnostic direction for improving the robustness of object detectors\.

## Submission history

From: Corentin FRIEDRICH \[[view email](https://arxiv.org/show-email/3030012a/2607.06592)\] \[via CCSD proxy\] **\[v1\]**Mon, 6 Jul 2026 08:33:28 UTC \(5,386 KB\)

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