Ensemble Adversarial Training: Attacks and Defenses

ICLR, Volume abs/1705.07204, 2018.

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Generic with respect to the application domain, suggest that adversarial training can be improved by decoupling the generation of adversarial examples from the model being trained

Abstract:

Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the modelu0027s loss. We show ...More

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