| Title | GeoDA: A Geometric Framework for Black-Box Adversarial Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Rahmati, A., Moosavi-Dezfooli, S.-M., Frossard, P., Dai, H. |
| Conference Name | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Date Published | jun |
| Keywords | adversarial examples, black box encryption, black-box adversarial attacks, black-box attack algorithm, black-box perturbations, black-box settings, carefully perturbed images, composability, Covariance matrices, data samples, decision boundary, Deep Networks, effective iterative algorithm, Estimation, gaussian distribution, geometric framework, image classification, Iterative methods, mean curvature, Measurement, Metrics, minimal perturbation, natural image classifiers, Neural networks, optimisation, pattern classification, Perturbation methods, pubcrawl, queries, query processing, Resiliency, Robustness |
| Abstract | Adversarial examples are known as carefully perturbed images fooling image classifiers. We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, each of them returning the top-1 label of the classifier. Our framework is based on the observation that the decision boundary of deep networks usually has a small mean curvature in the vicinity of data samples. We propose an effective iterative algorithm to generate query-efficient black-box perturbations with small p norms which is confirmed via experimental evaluations on state-of-the-art natural image classifiers. Moreover, for p=2, we theoretically show that our algorithm actually converges to the minimal perturbation when the curvature of the decision boundary is bounded. We also obtain the optimal distribution of the queries over the iterations of the algorithm. Finally, experimental results confirm that our principled black-box attack algorithm performs better than state-of-the-art algorithms as it generates smaller perturbations with a reduced number of queries. |
| DOI | 10.1109/CVPR42600.2020.00847 |
| Citation Key | rahmati_geoda_2020 |
GeoDA: A Geometric Framework for Black-Box Adversarial Attacks