  Machine learning systems based on deep neural networks (DNNs) have gained
mainstream adoption in many applications. Recently, however, DNNs are shown to
be vulnerable to adversarial example attacks with slight perturbations on the
inputs. Existing defense mechanisms against such attacks try to improve the
overall robustness of the system, but they do not differentiate different
targeted attacks even though the corresponding impacts may vary significantly.
To tackle this problem, we propose a novel configurable defense mechanism in
this work, wherein we are able to flexibly tune the robustness of the system
against different targeted attacks to satisfy application requirements. This is
achieved by refining the DNN loss function with an attack sensitive matrix to
represent the impacts of different targeted attacks. Experimental results on
CIFAR-10 and GTSRB data sets demonstrate the efficacy of the proposed solution.
