Adversarial Robustness

Adversarial Robustness

Why Your AI Model is Fragile (and How to Harden It)

You trained your model. It hits 98% test accuracy. You deploy it. Then someone puts a sticker on a stop sign, and your car thinks it's a speed limit. ...

Adversarial Robustness

What Adaptive Attack Budgets Reveal About Your Model's Blind Spots

You trained a model. It hits 98% probe accuracy. PGD with epsilon=8/255 barely drops it to 92%. You think: robust enough for deployment. Not always tr...

Adversarial Robustness

When Certified Robustness Guarantees Mislead About Real-World Attack Surface

Certified robustness guarantee sound impressive on paper. They promise that for any L p -bounded perturbaal, your model's prediction stays put. But he...

Adversarial Robustness

What to Fix First When Your Defense Fails Against Adaptive Attacks

You spent weeks hardening a model. Added adversarial trained, defensive distillation, maybe even a certified defense. Then someone runs an adaptive at...

Adversarial Robustness

Choosing the Right Threat Model Without Overfitting to ℓ∞

If you have ever uploaded a student project to a robustness leaderboard, you have probably trained against PGD-ℓ∞ with epsilon 8/255. It is the defaul...

Adversarial Robustness

When Adversarial Training Silently Destroys Your Calibration

Adversarial robustness and model calibration rarely share a headline. Most practitioners chase clean accuracy under attack, ignoring whether the model...