The design of additive imperceptible perturbations to the inputs of deep
classifiers to maximize their misclassification rates is a central focus of
adversarial machine learning. An alternative approach is to synthesize
adversarial examples from scratch using GAN-like structures, albeit with the
use of large amounts of training data. By contrast, this paper considers
one-shot synthesis of adversarial examples; the inputs are synthesized from
scratch to induce arbitrary soft predictions at the output of pre-trained
models, while simultaneously maintaining high similarity to specified inputs.
To this end, we present a problem that encodes objectives on the distance
between the desired and output distributions of the trained model and the
similarity between such inputs and the synthesized examples. We prove that the
formulated problem is NP-complete. Then, we advance a generative approach to
the solution in which the adversarial examples are obtained as the output of a
generative network whose parameters are iteratively updated by optimizing
surrogate loss functions for the dual-objective. We demonstrate the generality
and versatility of the framework and approach proposed through applications to
the design of targeted adversarial attacks, generation of decision boundary
samples, and synthesis of low confidence classification inputs. The approach is
further extended to an ensemble of models with different soft output
specifications. The experimental results verify that the targeted and
confidence reduction attack methods developed perform on par with
state-of-the-art algorithms