Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples
Table 1: Accuracies of defensively distilled networks trained with different Temperatures T against test set of the specified dataset.
['Dataset', '[ITALIC] T=10', '[ITALIC] T=20', '[ITALIC] T=30', '[ITALIC] T=40', 'w/o distillation']
The accuracies of the networks trained with and without defensive distillation are reported in Tab. The performance of the networks trained with distillation is slightly worse compared to the networks trained without distillation, which is expected according to Papernot et al. [Papernot2016_1]. The accuracy achieved in the experiment is comparable to others for the Amazon movie dataset (0.82-0.95 by Zhang et al. [Zhang2015]) and slightly lower for the AG dataset (0.83-0.92 by Zhang et al. [Zhang2015]).