Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples
Table 4: Transferability of generated adversarial examples to networks trained with defensive distillation with Temperature T.
['T', 'Dataset', 'Number tested', 'Success rate', 'Success rate w/o distillation']
Another aspect is robustness against transferability. Tab. datasets is a bit lower in most cases compared to the retrained network without distillation, with a difference of only 0.032−0.051. Based on this, we conclude that defensive distillation has only a small impact on transferability and does not, in fact, prevent it.