  The output of Deep Neural Networks (DNN) can be altered by a small
perturbation of the input in a black box setting by making multiple calls to
the DNN. However, the high computation and time required makes the existing
approaches unusable. This work seeks to improve the One-pixel (few-pixel)
black-box adversarial attacks to reduce the number of calls to the network
under attack. The One-pixel attack uses a non-gradient optimization algorithm
to find pixel-level perturbations under the constraint of a fixed number of
pixels, which causes the network to predict the wrong label for a given image.
We show through experimental results how the choice of the optimization
algorithm and initial positions to search can reduce function calls and
increase attack success significantly, making the attack more practical in
real-world settings.
