Improving Adversarial Robustness of Ensembles with Diversity Training

Understand

Deep Neural Networks are vulnerable to adversarial attacks even in settings where the attacker has no direct access to the model being attacked.

• Such attacks usually rely on the principle of transferability, whereby an attack crafted on a surrogate model tends to transfer to the target model.
• We show that an ensemble of models with misaligned loss gradients can provide an effective defense against transfer-based attacks.
• Our key insight is that an adversarial example is less likely to fool multiple models in the ensemble if their loss functions do not increase in a correlated fashion.