Reinforcement Learning Training Disrupts Gradient-Based Adversarial Attacks on Neural Networks
Researchers found that training image classifiers using reinforcement learning (RL) significantly disrupts gradient-based adversarial attacks by destabilizing gradient directions and reducing gradient magnitudes. The study tested this approach across CIFAR-10, CIFAR-100, and ImageNet-100 datasets with multiple neural network architectures. This finding suggests RL-based training could complement existing adversarial defenses and improve robustness against multiple attack types.
A new arXiv preprint investigates how reinforcement learning can defend deep neural networks against gradient-based adversarial attacks, which exploit gradient information to craft adversarial perturbations. Through systematic experiments, the researchers demonstrate that RL-trained classifiers significantly disrupt the gradient structure attackers rely on, making gradient-based attacks like PGD and AutoAttack fail within practical iteration budgets. Mechanistic analysis reveals that RL acts as an implicit regularizer, producing models with unstable gradient directions and smaller magnitudes. When combined with adversarial training (RL-adv), the approach provides dual-layer defense: RL degrades gradient information while adversarial training strengthens decision boundaries. The hybrid RL-adv method outperforms standard supervised learning with adversarial training (SL-adv) across gradient-based, transfer-based, and query-based attacks, suggesting that combining RL's gradient-regularization properties with supervised learning's efficiency could advance neural network robustness.
What's missing
The study's limitations regarding computational cost of RL training compared to standard supervised learning, generalization to other domains beyond image classification, and potential vulnerabilities to adaptive attacks specifically designed against RL-induced gradient disruption are not discussed in the abstract.
What different sources said
- arXiv cs.AICenter
Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization
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