Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics
arXiv:2606.22054v2 Announce Type: replace-cross Abstract: Detectors for GNSS radio-frequency impairments (jamming, spoofing, multipath) are usually reported with a single AUC measured on the distribution they were tuned on. That number falls once conditions move, and the size of the drop is rarely known in advance because labelled field data is scarce. We ask whether this optimism can be predicted before any out-of-distribution data is seen. On an open, parameter-grounded synthetic testbed with a tunable severity shift, we evaluate thirteen detectors (five physics baselines, full-feature logis
The proliferation of AI systems in critical infrastructure like GNSS necessitates more robust, real-world performance prediction to ensure reliability and trust.
Predicting the performance degradation of AI models when deployed in novel environments addresses a fundamental challenge for real-world AI adoption, particularly in sensitive defense applications.
The ability to anticipate how AI-powered detectors will perform outside their training distribution fundamentally alters the risk assessment for deploying such systems more broadly.
- · AI model developers with robust testing methodologies
- · GNSS-reliant industries
- · Defense and aerospace sectors
- · AI developers relying solely on in-distribution metrics
- · Adversaries using stealthy jamming/spoofing
- · Traditional, less adaptable detection systems
This research will lead to more resilient AI models for critical applications by predicting out-of-distribution performance.
Improved confidence in AI deployment for national security may accelerate AI integration into defense systems, impacting strategic capabilities.
The methodology could generalize to other AI domains, fostering new certification standards and ethical guidelines for AI safety beyond its initial training parameters.
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Read at arXiv cs.LG