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Who's Afraid of Adversarial Transferability?
By combining theoretical reasoning with a series of empirical results, we show that it is practically impossible to predict whether a given adversarial example is transferable to a specific target model in a black-box setting, hence questioning the validity of adversarial transferability as a real-life attack tool for adversaries that are sensitive to the cost of a failed attack.
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples
We, however, argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data such as images.
Adversarial robustness via stochastic regularization of neural activation sensitivity
Recent works have shown that the input domain of any machine learning classifier is bound to contain adversarial examples.
Why Blocking Targeted Adversarial Perturbations Impairs the Ability to Learn
We show that contrary to commonly held belief, the ability to bypass defensive distillation is not dependent on an attack's level of sophistication.
Detecting Adversarial Perturbations Through Spatial Behavior in Activation Spaces
We leverage those classifiers to produce a sequence of class labels for each nonperturbed input sample and estimate the a priori probability for a class label change between one activation space and another.
