Improving Adversarial Robustness of Ensembles with Diversity Training
This addresses security concerns for AI systems by enhancing defense against adversarial attacks, though it is incremental as it builds on existing ensemble methods.
The paper tackles the vulnerability of deep neural networks to transfer-based adversarial attacks by proposing Diversity Training, a method to train ensembles with uncorrelated loss functions, resulting in significantly improved adversarial robustness.
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. To this end, we propose Diversity Training, a novel method to train an ensemble of models with uncorrelated loss functions. We show that our method significantly improves the adversarial robustness of ensembles and can also be combined with existing methods to create a stronger defense.