Zonotope-Based Active Exposure of Stealthy Deception Attacks in Sensor-Fusion Systems

What the math gives to ML

The paper provides a constructive active-exposure mechanism: represent bounded-noise state/output predictions and attack hypotheses as zonotopes, then optimize bounded control-channel perturbations so the admissible and attack output sets become separated. Its transferable asset is not merely interval uncertainty, but a receding-horizon controller that actively creates a measurable detection margin against multiple simultaneous sensor compromises. For neural sensor-fusion systems, the same mechanism can inject small probing perturbations into inputs or intermediate modalities and use Jacobian-based zonotopes to select perturbations that maximally separate trusted and corrupted-feature predictions. The key falsifiable signature is a sharp transition from overlapping sets to a positive separation margin as exposure budget increases.