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Runbo Li

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2 papers

2 author rows

AAAI Conference 2026 Conference Paper

False Positives Matter: Multidimensional Localization Evaluation and Training-Free Explainable Adversarial Patch Defense

Lihua Jing
Rui Wang
Jinwen Zhong
Runbo Li
Zixuan Zhu

Adversarial patch attacks pose a significant threat to visual systems. While current patch purification-based defense methods enhance core metrics of visual perception models, they overlook the critical issue of false positive patches, severely compromising image usability. This paper reveals the inadequacy of existing evaluations for adversarial patch defenses, and pioneers a multidimensional adversarial patch localization evaluation framework, which comprehensively quantifies false positives, recall capability, and overall localization accuracy, providing a novel perspective for comparative analysis within the field. Furthermore, building upon the observation that false positives stem from a lack of semantic understanding, we propose a Semantic-Aware Training-free Explainable Defense method (SATED). SATED achieves zero-shot patch localization, false detection correction, and decision explanation by constructing a patch reasoning chain, while simultaneously performing integrated text-guided patch inpainting. Extensive experiments across digital and physical scenarios, detection and segmentation tasks, and diverse adversarial patches, demonstrate that our method significantly reduces false positives and doubles the overall patch localization accuracy, boosting both the generalizability and explainability of the defense.

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ICRA Conference 2025 Conference Paper

Multi-Task Robustness Enhancement Framework against Various Adversarial Patches

Lihua Jing
Rui Wang 0032
Runbo Li
Zixuan Zhu 0002
Xingxing Wei

Autonomous systems leveraging visual perception face a rising threat from adversarial patches, jeopardizing their robustness. Existing defense methods adaptable to various pre-trained models typically rely on observed patch characteristics or prior attack data, having difficulty adapting to new threats. This study innovatively focuses on modeling patch attack behavior instead of existing patches, proposing a unified robustness enhancement framework against various adversarial patches. Through self-supervised learning, we accurately locate diverse adversarial patches without prior attack knowledge. Furthermore, we introduce an efficient adaptive patch inpainting method to mitigate patch impact while maintaining visual coherence. Experiments show that our methods effectively boost the robustness of visual perception models against various adversarial patches across different tasks.

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