  State-of-the-art object recognition Convolutional Neural Networks (CNNs) are
shown to be fooled by image agnostic perturbations, called universal
adversarial perturbations. It is also observed that these perturbations
generalize across multiple networks trained on the same target data. However,
these algorithms require training data on which the CNNs were trained and
compute adversarial perturbations via complex optimization. The fooling
performance of these approaches is directly proportional to the amount of
available training data. This makes them unsuitable for practical attacks since
its unreasonable for an attacker to have access to the training data. In this
paper, for the first time, we propose a novel data independent approach to
generate image agnostic perturbations for a range of CNNs trained for object
recognition. We further show that these perturbations are transferable across
multiple network architectures trained either on same or different data. In the
absence of data, our method generates universal adversarial perturbations
efficiently via fooling the features learned at multiple layers thereby causing
CNNs to misclassify. Experiments demonstrate impressive fooling rates and
surprising transferability for the proposed universal perturbations generated
without any training data.
