  Adversarial attacks against Deep Neural Networks have been widely studied.
One significant feature that makes such attacks particularly powerful is
transferability, where the adversarial examples generated from one model can be
effective against other similar models as well. A large number of works have
been done to increase the transferability. However, how to decrease the
transferability and craft malicious samples only for specific target models are
not explored yet.
  In this paper, we design novel attack methodologies to generate adversarial
examples with controllable non-transferability. With these methods, an
adversary can efficiently produce precise adversarial examples to attack a set
of target models he desires, while keeping benign to other models. The first
method is Reversed Loss Function Ensemble, where the adversary can craft
qualified examples from the gradients of a reversed loss function. This
approach is effective for the white-box and gray-box settings. The second
method is Transferability Classification: the adversary trains a
transferability-aware classifier from the perturbations of adversarial
examples. This classifier further provides the guidance for the generation of
non-transferable adversarial examples. This approach can be applied to the
black-box scenario. Evaluation results demonstrate the effectiveness and
efficiency of our proposed methods. This work opens up a new route for
generating adversarial examples with new features and applications.
