SegPAR: Class-Centric Decision-Based Sparse Attack for Semantic Segmentation

• URL: http://arxiv.org/abs/2608.11285v1
• Date: Tue, 11 Aug 2026 14:44:33 GMT
• Title: SegPAR: Class-Centric Decision-Based Sparse Attack for Semantic Segmentation
• Abstract summary: We adapt the most representative decision-based black-box sparse attacks from the classification domain to serve as baselines.<n>SegPAR is a novel decision-based framework that shifts to a class-centric exploration paradigm.<n>Experiments show that SegPAR significantly outperforms black-box baselines in sparsity efficiency and MIoU reduction.
• Score: 2.3332469289621787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the practical relevance of sparse decision-based black-box threats, they have received limited attention in semantic segmentation. To bridge this gap, we adapt the most representative decision-based black-box sparse attacks from the classification domain to serve as baselines, establishing a rigorous benchmark for this underexplored setting. In this context, we demonstrate that one of the existing methods suffers from severe query inefficiency due to its image-centric pixel accumulation, which rapidly exhausts query budgets across the vast image space. To overcome this, we propose SegPAR, a novel decision-based framework that shifts to a class-centric exploration paradigm. Furthermore, to eliminate the misleading feedback generated by standard decision rewards during pixel accumulation, we introduce a novel discrepancy reward. Extensive experiments show that SegPAR significantly outperforms black-box baselines in sparsity efficiency and MIoU reduction, while remaining competitive with white-box sparse attacks. Code is available at \href{https://github.com/KAU-QuantumAILab/SegPAR}{https://github.com/KAU-QuantumAILab/SegPAR}.

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