Name
Abstract | ||
|---|---|---|
We propose a powerful second-order attack method that outperforms existing attack methods on reducing the accuracy of state-of-the-art defense models based on adversarial training. The effectiveness of our attack method motivates an investigation of provable robustness of a defense model. To this end, we introduce a framework that allows one to obtain a certifiable lower bound on the prediction accuracy against adversarial examples. We conduct experiments to show the effectiveness of our attack method. At the same time, our defense models obtain higher accuracies compared to previous works under our proposed attack. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Upper and lower bounds,Robustness (computer science),Artificial intelligence,Mathematics,Machine learning,Adversarial system |
DocType | Volume | Citations |
Journal | abs/1809.03113 | 1 |
PageRank | References | Authors |
0.34 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bai Li | 1 | 10 | 2.82 |
| Changyou Chen | 2 | 365 | 36.95 |
| Wenlin Wang | 3 | 51 | 7.06 |
| L. Carin | 4 | 4603 | 339.36 |