Feinman et al. use dropout to compute an uncertainty measure that helps to identify adversarial examples. Their so-called Bayesian Neural Network Uncertainty is computed as follows:
where $\{\hat{y}_1,\ldots,\hat{y}_T\}$ is a set of stochastic predictions (i.e. predictions with different noise patterns in the dropout layers). Here, is can easily be seen that this measure corresponds to a variance computation where the first term is correlation and the second term is the product of expectations. In Figure 1, the authors illustrate the distributions of this uncertainty measure for regular training samples, adversarial samples and noisy samples for two attacks (BIM and JSMA, see paper for details).
Figure 1: Uncertainty distributions for two attacks (BIM and JSMA, see paper for details) and normal samples, adversarial samples and noisy samples.
Feinman et al. use dropout to compute an uncertainty measure that helps to identify adversarial examples. Their so-called Bayesian Neural Network Uncertainty is computed as follows:
$\frac{1}{T} \sum_{i=1}^T \hat{y}_i^T \hat{y}_i - \left(\sum_{i=1}^T \hat{y}_i\right)\left(\sum_{i=1}^T \hat{y}_i\right)$
where $\{\hat{y}_1,\ldots,\hat{y}_T\}$ is a set of stochastic predictions (i.e. predictions with different noise patterns in the dropout layers). Here, is can easily be seen that this measure corresponds to a variance computation where the first term is correlation and the second term is the product of expectations. In Figure 1, the authors illustrate the distributions of this uncertainty measure for regular training samples, adversarial samples and noisy samples for two attacks (BIM and JSMA, see paper for details).
Figure 1: Uncertainty distributions for two attacks (BIM and JSMA, see paper for details) and normal samples, adversarial samples and noisy samples.