On the Effect of Adversarial Training Against Invariance-based Adversarial Examples
This work addresses a previously unexplored problem in adversarial machine learning for researchers and practitioners, though it is incremental as it builds on prior methods for generating adversarial examples.
The study tackled the problem of ensuring robustness against invariance-based adversarial examples by investigating the impact of adversarial training on a CNN, finding that simultaneous training with both invariance-based and perturbation-based examples achieves relatively high robustness against both types.
Adversarial examples are carefully crafted attack points that are supposed to fool machine learning classifiers. In the last years, the field of adversarial machine learning, especially the study of perturbation-based adversarial examples, in which a perturbation that is not perceptible for humans is added to the images, has been studied extensively. Adversarial training can be used to achieve robustness against such inputs. Another type of adversarial examples are invariance-based adversarial examples, where the images are semantically modified such that the predicted class of the model does not change, but the class that is determined by humans does. How to ensure robustness against this type of adversarial examples has not been explored yet. This work addresses the impact of adversarial training with invariance-based adversarial examples on a convolutional neural network (CNN). We show that when adversarial training with invariance-based and perturbation-based adversarial examples is applied, it should be conducted simultaneously and not consecutively. This procedure can achieve relatively high robustness against both types of adversarial examples. Additionally, we find that the algorithm used for generating invariance-based adversarial examples in prior work does not correctly determine the labels and therefore we use human-determined labels.