  Visual-based recommender systems (VRSs) enhance recommendation performance by
integrating users' feedback with the visual features of product images
extracted from a deep neural network (DNN). Recently, human-imperceptible
images perturbations, defined \textit{adversarial attacks}, have been
demonstrated to alter the VRSs recommendation performance, e.g., pushing/nuking
category of products. However, since adversarial training techniques have
proven to successfully robustify DNNs in preserving classification accuracy, to
the best of our knowledge, two important questions have not been investigated
yet: 1) How well can these defensive mechanisms protect the VRSs performance?
2) What are the reasons behind ineffective/effective defenses? To answer these
questions, we define a set of defense and attack settings, as well as
recommender models, to empirically investigate the efficacy of defensive
mechanisms. The results indicate alarming risks in protecting a VRS through the
DNN robustification. Our experiments shed light on the importance of visual
features in very effective attack scenarios. Given the financial impact of VRSs
on many companies, we believe this work might rise the need to investigate how
to successfully protect visual-based recommenders. Source code and data are
available at
https://anonymous.4open.science/r/868f87ca-c8a4-41ba-9af9-20c41de33029/.
