Name
Abstract | ||
|---|---|---|
Although deep convolutional neural networks (CNNs) have demonstrated remarkable performance on multiple computer vision tasks, researches on adversarial learning have shown that deep models are vulnerable to adversarial examples, which are crafted by adding visually imperceptible perturbations to the input images. Most of the existing adversarial attack methods only create a single adversarial exa... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TPAMI.2020.3032061 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Training,Monte Carlo methods,Space exploration,Robustness,Markov processes,Cats,Iterative methods | Journal | 44 |
Issue | ISSN | Citations |
4 | 0162-8828 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongjun Wang | 1 | 0 | 1.35 |
| Guanbin Li | 2 | 259 | 37.61 |
| Xiaobai Liu | 3 | 800 | 40.79 |
| Liang Lin | 4 | 3007 | 151.07 |