  Adversarial attacks, e.g., adversarial perturbations of the input and
adversarial samples, pose significant challenges to machine learning and deep
learning techniques, including interactive recommendation systems. The latent
embedding space of those techniques makes adversarial attacks difficult to
detect at an early stage. Recent advance in causality shows that counterfactual
can also be considered one of ways to generate the adversarial samples drawn
from different distribution as the training samples. We propose to explore
adversarial examples and attack agnostic detection on reinforcement
learning-based interactive recommendation systems. We first craft different
types of adversarial examples by adding perturbations to the input and
intervening on the casual factors. Then, we augment recommendation systems by
detecting potential attacks with a deep learning-based classifier based on the
crafted data. Finally, we study the attack strength and frequency of
adversarial examples and evaluate our model on standard datasets with multiple
crafting methods. Our extensive experiments show that most adversarial attacks
are effective, and both attack strength and attack frequency impact the attack
performance. The strategically-timed attack achieves comparative attack
performance with only 1/3 to 1/2 attack frequency. Besides, our black-box
detector trained with one crafting method has the generalization ability over
several other crafting methods.
