  Deep learning models are susceptible to input specific noise, called
adversarial perturbations. Moreover, there exist input-agnostic noise, called
Universal Adversarial Perturbations (UAP) that can affect inference of the
models over most input samples. Given a model, there exist broadly two
approaches to craft UAPs: (i) data-driven: that require data, and (ii)
data-free: that do not require data samples. Data-driven approaches require
actual samples from the underlying data distribution and craft UAPs with high
success (fooling) rate. However, data-free approaches craft UAPs without
utilizing any data samples and therefore result in lesser success rates. In
this paper, for data-free scenarios, we propose a novel approach that emulates
the effect of data samples with class impressions in order to craft UAPs using
data-driven objectives. Class impression for a given pair of category and model
is a generic representation (in the input space) of the samples belonging to
that category. Further, we present a neural network based generative model that
utilizes the acquired class impressions to learn crafting UAPs. Experimental
evaluation demonstrates that the learned generative model, (i) readily crafts
UAPs via simple feed-forwarding through neural network layers, and (ii)
achieves state-of-the-art success rates for data-free scenario and closer to
that for data-driven setting without actually utilizing any data samples.
