  This paper explores the scenarios under which an attacker can claim that
'Noise and access to the softmax layer of the model is all you need' to steal
the weights of a convolutional neural network whose architecture is already
known. We were able to achieve 96% test accuracy using the stolen MNIST model
and 82% accuracy using the stolen KMNIST model learned using only i.i.d.
Bernoulli noise inputs. We posit that this theft-susceptibility of the weights
is indicative of the complexity of the dataset and propose a new metric that
captures the same. The goal of this dissemination is to not just showcase how
far knowing the architecture can take you in terms of model stealing, but to
also draw attention to this rather idiosyncratic weight learnability aspects of
CNNs spurred by i.i.d. noise input. We also disseminate some initial results
obtained with using the Ising probability distribution in lieu of the i.i.d.
Bernoulli distribution.
