Towards Evaluating the Robustness of Chinese BERT Classifiers
Table 3. Classifier accuracy after transerability-based blackbox attack on other BERT-based classifiers.
['Dataset', 'AdvChar (target)', 'AdvChar (untarget)']
We find the adversarial text can still substantially affect the accuracy of blackbox models. In addition, the targeted attack success rate turns out to be stronger than the untargeted attack. Particularly, the targeted adversarial examples on the Wechat dataset can make the blackbox BERT classifier performance drop from 88.2\% to 14.3\%.