GenAI-FDIA benchmarks physics-informed generative models for stealthy FDIA synthesis on IEEE testbeds, reports high evasion rates, and introduces an inference-time harmoniser plus warm-up schedules to fix projection displacement and covariance collapse.
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Pre-trained models are added late in projects, accumulate rather than get replaced, and change three times less often than libraries, with distinct documentation driven by capability needs and testing uncertainty.
citing papers explorer
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GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks
GenAI-FDIA benchmarks physics-informed generative models for stealthy FDIA synthesis on IEEE testbeds, reports high evasion rates, and introduces an inference-time harmoniser plus warm-up schedules to fix projection displacement and covariance collapse.
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When AI Models Become Dependencies: Studying the Evolution of Pre-Trained Model Reuse in Downstream Software Systems
Pre-trained models are added late in projects, accumulate rather than get replaced, and change three times less often than libraries, with distinct documentation driven by capability needs and testing uncertainty.