  Deep neural networks (DNNs) have recently achieved state-of-the-art
performance and provide significant progress in many machine learning tasks,
such as image classification, speech processing, natural language processing,
etc. However, recent studies have shown that DNNs are vulnerable to adversarial
attacks. For instance, in the image classification domain, adding small
imperceptible perturbations to the input image is sufficient to fool the DNN
and to cause misclassification. The perturbed image, called \textit{adversarial
example}, should be visually as close as possible to the original image.
However, all the works proposed in the literature for generating adversarial
examples have used the $L_{p}$ norms ($L_{0}$, $L_{2}$ and $L_{\infty}$) as
distance metrics to quantify the similarity between the original image and the
adversarial example. Nonetheless, the $L_{p}$ norms do not correlate with human
judgment, making them not suitable to reliably assess the perceptual
similarity/fidelity of adversarial examples. In this paper, we present a
database for visual fidelity assessment of adversarial examples. We describe
the creation of the database and evaluate the performance of fifteen
state-of-the-art full-reference (FR) image fidelity assessment metrics that
could substitute $L_{p}$ norms. The database as well as subjective scores are
publicly available to help designing new metrics for adversarial examples and
to facilitate future research works.
