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Two-stage uncertainty-aware adversarial patch attack for semantic segmentation
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AI
DOI：10.1108/IJICC-11-2025-0774.png)
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Abstract
En 中文
PurposeThis work aims to investigate and improve adversarial patch attacks for semantic segmentation, a task increasingly deployed in security-critical applications. Existing attacks often overlook pixel-level uncertainty and spatial variation, resulting in inefficient optimization and limited effectiveness. The purpose of this study is to design an uncertainty-aware attack framework that better identifies and exploits structurally vulnerable regions in segmentation models.Design/methodology/approachWe propose a two-stage uncertainty-aware adversarial patch attack framework. The first stage computes pixel-wise entropy to identify locally uncertain regions. The second stage applies a confidence-based inter-pixel weighting strategy that prioritizes vulnerable pixels by comparing their confidence to a global statistical threshold. These components are unified into a dynamic loss reweighting mechanism. Experiments are conducted on Cityscapes and BDD100 K using ICNet, DDRNet, and SegFormer.FindingsExperimental results show that the proposed method outperforms existing patch-based attacks such as SSAP. By effectively targeting uncertain and structurally vulnerable regions, our method achieves stronger degradation of segmentation performance, with mIoU reduced to as low as 8%. The results demonstrate both high attack effectiveness and strong cross-dataset and cross-model generalization.Originality/valueThis work is the first to incorporate pixel-level uncertainty into adversarial patch optimization for semantic segmentation. Unlike prior patch-based attacks that treat all pixels uniformly, our method explicitly models local entropy and confidence-driven spatial variation, enabling more targeted and effective perturbation. The proposed dynamic loss reweighting framework provides a novel perspective on exploiting structural vulnerabilities in dense prediction tasks. This approach offers both theoretical and practical value for understanding segmentation robustness and designing stronger uncertainty-guided attacks.
Keywords:
Adversarial patch attack
Semantic segmentation
Pixel uncertainty
Confidence ranking
Autonomous driving security
Journal
I
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2.6
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40
Citations:
543