Boosting the Transferability of Adversarial Examples via Local Mixup and
  Adaptive Step Size
Junlin Liu Xinchen Lyu
Adversarial examples are one critical security threat to various visual
applications, where injected human-imperceptible perturbations can confuse the
output.Generating transferable adversarial examples in the black-box setting is
crucial but challenging in practice. Existing input-diversity-based methods
adopt different image transformations, but may be inefficient due to
insufficient input diversity and an identical perturbation step size. Motivated
by the fact that different image regions have distinctive weights in
classification, this paper proposes a black-box adversarial generative
framework by jointly designing enhanced input diversity and adaptive step
sizes. We design local mixup to randomly mix a group of transformed adversarial
images, strengthening the input diversity. For precise adversarial generation,
we project the perturbation into the $tanh$ space to relax the boundary
constraint. Moreover, the step sizes of different regions can be dynamically
adjusted by integrating a second-order momentum.Extensive experiments on
ImageNet validate that our framework can achieve superior transferability
compared to state-of-the-art baselines.