LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection
This paper introduces LipSSD, a Lipschitz-constrained Single Shot MultiBox Detector (SSD) that improves adversarial robustness by design. On Pascal VOC, adversarially trained LipSSD improves mAP@50 on unseen attacks by up to 15 points over standard adversarially trained SSD, and shows robustness gains on safety-critical datasets LARD and KITTI while largely preserving clean performance.
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[Submitted on 6 Jul 2026]
Title:LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection
View a PDF of the paper titled LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection, by Vincent L\'eb\'e and 7 other authors
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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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.06592 [cs.CV]
(or arXiv:2607.06592v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.06592
arXiv-issued DOI via DataCite
Submission history
From: Corentin FRIEDRICH [view email] [via CCSD proxy] [v1] Mon, 6 Jul 2026 08:33:28 UTC (5,386 KB)
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