Interpretation of Neural Networks is Susceptible to Universal Adversarial Perturbations
This reveals a critical vulnerability in widely-used interpretation methods for neural networks, which could undermine trust in AI systems, but is incremental as it extends known adversarial attacks to the interpretation domain.
The paper demonstrates that gradient-based saliency maps for interpreting neural networks are vulnerable to universal adversarial perturbations (UPIs) that can alter interpretations across many test samples, and proposes two methods to compute such UPIs with numerical validation on standard image datasets.
Interpreting neural network classifiers using gradient-based saliency maps has been extensively studied in the deep learning literature. While the existing algorithms manage to achieve satisfactory performance in application to standard image recognition datasets, recent works demonstrate the vulnerability of widely-used gradient-based interpretation schemes to norm-bounded perturbations adversarially designed for every individual input sample. However, such adversarial perturbations are commonly designed using the knowledge of an input sample, and hence perform sub-optimally in application to an unknown or constantly changing data point. In this paper, we show the existence of a Universal Perturbation for Interpretation (UPI) for standard image datasets, which can alter a gradient-based feature map of neural networks over a significant fraction of test samples. To design such a UPI, we propose a gradient-based optimization method as well as a principal component analysis (PCA)-based approach to compute a UPI which can effectively alter a neural network's gradient-based interpretation on different samples. We support the proposed UPI approaches by presenting several numerical results of their successful applications to standard image datasets.