针对资源受限场景，提出高效对抗鲁棒性测试新方法。
DDSA: Dual-Domain Strategic Attack for Spatial-Temporal Efficiency in Adversarial Robustness Testing
- 按重要性选择关键帧，仅对关键区域施加扰动
- 节省大量计算资源，同时保持攻击有效性
- 适合实时系统中大规模图像流的快速检测
资源受限的应用中，图像传输与处理系统易受对抗扰动影响，导致任务特定目标分类失效。现有鲁棒性测试方法依赖逐帧全图扰动，计算开销巨大，难以在需实时处理海量图像流的场景中部署。本文提出双域策略攻击框架DDSA（Dual-Domain Strategic Attack），通过时间选择性与空间精准性优化测试效率。引入场景感知触发函数，基于类别优先级与模型不确定性识别需评估的关键帧；结合可解释AI技术定位关键像素区域，实施针对性扰动。该双域方法在显著降低时空资源消耗的同时，维持攻击效果。框架使大规模、实时的对抗鲁棒性测试在资源受限环境中具备实际可行性，计算效率直接关系任务成败。
原文摘要 · Abstract (English)
Image transmission and processing systems in resource-critical applications face significant challenges from adversarial perturbations that compromise mission-specific object classification. Current robustness testing methods require excessive computational resources through exhaustive frame-by-frame processing and full-image perturbations, proving impractical for large-scale deployments where massive image streams demand immediate processing. This paper presents DDSA (Dual-Domain Strategic Attack), a resource-efficient adversarial robustness testing framework that optimizes testing through temporal selectivity and spatial precision. We introduce a scenario-aware trigger function that identifies critical frames requiring robustness evaluation based on class priority and model uncertainty, and employ explainable AI techniques to locate influential pixel regions for targeted perturbation. Our dual-domain approach achieves substantial temporal-spatial resource conservation while maintaining attack effectiveness. The framework enables practical deployment of comprehensive adversarial robustness testing in resource-constrained real-time applications where computational efficiency directly impacts mission success.
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