Robustness & Security

Adversarial Threat Evaluation

The system is evaluated against adversarial machine learning attacks including FGSM and PGD, as well as quantum-aware perturbations such as parameter noise and decoherence simulation.

These evaluations measure how detection systems behave under intentional manipulation, which is critical for understanding real breach risk.

Why Robustness Matters to Leadership

Accuracy alone does not equal security. Robustness under attack determines breach probability.

Hybrid quantum–neural models demonstrate:

Smaller performance degradation
Greater prediction stability
Reduced risk exposure during incidents