Securing Visually-Aware Recommender Systems: An Adversarial Image
  Reconstruction and Detection Framework
Minglei Yin Bin Liu Neil Zhenqiang Gong Xin Li
With rich visual data, such as images, becoming readily associated with
items, visually-aware recommendation systems (VARS) have been widely used in
different applications. Recent studies have shown that VARS are vulnerable to
item-image adversarial attacks, which add human-imperceptible perturbations to
the clean images associated with those items. Attacks on VARS pose new security
challenges to a wide range of applications such as e-Commerce and social
networks where VARS are widely used. How to secure VARS from such adversarial
attacks becomes a critical problem. Currently, there is still a lack of
systematic study on how to design secure defense strategies against visual
attacks on VARS. In this paper, we attempt to fill this gap by proposing an
adversarial image reconstruction and detection framework to secure VARS. Our
proposed method can simultaneously (1) secure VARS from adversarial attacks
characterized by local perturbations by image reconstruction based on global
vision transformers; and (2) accurately detect adversarial examples using a
novel contrastive learning approach. Meanwhile, our framework is designed to be
used as both a filter and a detector so that they can be jointly trained to
improve the flexibility of our defense strategy to a variety of attacks and
VARS models. We have conducted extensive experimental studies with two popular
attack methods (FGSM and PGD). Our experimental results on two real-world
datasets show that our defense strategy against visual attacks is effective and
outperforms existing methods on different attacks. Moreover, our method can
detect adversarial examples with high accuracy.