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.

翻译：资源关键型应用中的图像传输与处理系统面临对抗性扰动的严峻挑战，这些扰动会损害任务特定目标分类的可靠性。现有鲁棒性测试方法需通过逐帧穷举处理和全图像扰动消耗过量计算资源，在大规模部署场景中，海量图像流需即时处理，此类方法被证明不具备实用性。本文提出DDSA（双域策略攻击），一种资源高效的对抗鲁棒性测试框架，通过时间选择性与空间精确性优化测试过程。我们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要鲁棒性评估的关键帧，并采用可解释人工智能技术定位具有影响力的像素区域以实施定向扰动。我们的双域方法在保持攻击效能的同时，实现了显著的时空资源节约。该框架使得在计算效率直接影响任务成败的资源受限实时应用中，能够实际部署全面的对抗鲁棒性测试。