Beyond Explainability: Leveraging Interpretability for Improved Adversarial Learning
This work addresses adversarial robustness in machine learning by applying interpretability beyond explainability, though it appears incremental as it builds on existing methods for adversarial attacks.
The paper tackles the problem of generating adversarial examples by using gradient-based interpretability to guide spatially constrained one-pixel perturbations, resulting in improved convergence speed and successful, visually imperceptible attacks on benchmark datasets.
In this study, we propose the leveraging of interpretability for tasks beyond purely the purpose of explainability. In particular, this study puts forward a novel strategy for leveraging gradient-based interpretability in the realm of adversarial examples, where we use insights gained to aid adversarial learning. More specifically, we introduce the concept of spatially constrained one-pixel adversarial perturbations, where we guide the learning of such adversarial perturbations towards more susceptible areas identified via gradient-based interpretability. Experimental results using different benchmark datasets show that such a spatially constrained one-pixel adversarial perturbation strategy can noticeably improve the speed of convergence as well as produce successful attacks that were also visually difficult to perceive, thus illustrating an effective use of interpretability methods for tasks outside of the purpose of purely explainability.