  Deep Learning has become popular due to its vast applications in almost all
domains. However, models trained using deep learning are prone to failure for
adversarial samples and carry a considerable risk in sensitive applications.
Most of these adversarial attack strategies assume that the adversary has
access to the training data, the model parameters, and the input during
deployment, hence, focus on perturbing the pixel level information present in
the input image.
  Adversarial Patches were introduced to the community which helped in bringing
out the vulnerability of deep learning models in a much more pragmatic manner
but here the attacker has a white-box access to the model parameters. Recently,
there has been an attempt to develop these adversarial attacks using black-box
techniques. However, certain assumptions such as availability large training
data is not valid for a real-life scenarios. In a real-life scenario, the
attacker can only assume the type of model architecture used from a select list
of state-of-the-art architectures while having access to only a subset of input
dataset. Hence, we propose an black-box adversarial attack strategy that
produces adversarial patches which can be applied anywhere in the input image
to perform an adversarial attack.
