A digital twin water forecasting LSTM is shown to be vulnerable to FGSM and PGD attacks, and Learning Automata variants that adapt epsilon push MAPE above 35%, but no detection experiment supports the stealth claim.
Trojan attack and defense for deep learning-based navigation systems of unmanned aerial vehicles,
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The Dark Side of Digital Twins: Adversarial Attacks on AI-Driven Water Forecasting
A digital twin water forecasting LSTM is shown to be vulnerable to FGSM and PGD attacks, and Learning Automata variants that adapt epsilon push MAPE above 35%, but no detection experiment supports the stealth claim.