Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach
This work addresses the problem of improving adversarial robustness for machine learning models, particularly in computer vision, by reducing training time while maintaining or enhancing accuracy, though it is incremental as it builds on existing adversarial training frameworks.
The paper tackles the slow training time of adversarial training methods by proposing a non-iterative, teacher-guided curriculum learning approach that restricts perturbations to object pixels, achieving significant performance gains with 10-20% extra effort and outperforming other methods in adversarial and natural accuracy on datasets like CIFAR-10, CIFAR-100, and TinyImageNet.
Current SOTA adversarially robust models are mostly based on adversarial training (AT) and differ only by some regularizers either at inner maximization or outer minimization steps. Being repetitive in nature during the inner maximization step, they take a huge time to train. We propose a non-iterative method that enforces the following ideas during training. Attribution maps are more aligned to the actual object in the image for adversarially robust models compared to naturally trained models. Also, the allowed set of pixels to perturb an image (that changes model decision) should be restricted to the object pixels only, which reduces the attack strength by limiting the attack space. Our method achieves significant performance gains with a little extra effort (10-20%) over existing AT models and outperforms all other methods in terms of adversarial as well as natural accuracy. We have performed extensive experimentation with CIFAR-10, CIFAR-100, and TinyImageNet datasets and reported results against many popular strong adversarial attacks to prove the effectiveness of our method.