DDSA: Dual-Domain Strategic Attack for Spatial-Temporal Efficiency in Adversarial Robustness Testing

Adversarial testing can be made dramatically more efficient: DDSA reduces computational demands by selectively testing only the most critical frames in video streams rather than analyzing every frame, making robustness testing practical for real-world applications with limited computing resources.

Smart targeting improves testing without sacrificing rigor: The framework uses explainable AI to identify which specific pixels are most important to test, rather than perturbing entire images. This "spatial precision" approach maintains attack effectiveness while conserving resources—a key consideration for responsible AI deployment where testing shouldn't require prohibitive computational costs.

Prioritization based on mission-critical factors matters: The framework's "scenario-aware trigger function" focuses testing effort on frames involving high-priority object classes and moments of high model uncertainty, meaning robustness testing resources are directed where they matter most for system safety and reliability.

Resource efficiency enables broader security testing: By making adversarial robustness testing computationally practical, DDSA could enable more organizations—particularly those with limited budgets—to thoroughly test their AI systems before deployment, potentially improving overall AI safety across industries.

Explainability supports accountability: The use of explainable AI techniques to identify which image regions are tested creates transparency about how and why systems are being evaluated for vulnerabilities, supporting more trustworthy AI development practices.