  Multi-Instance Learning (MIL) is a recent machine learning paradigm which is
immensely useful in various real-life applications, like image analysis, video
anomaly detection, text classification, etc. It is well known that most of the
existing machine learning classifiers are highly vulnerable to adversarial
perturbations. Since MIL is a weakly supervised learning, where information is
available for a set of instances, called bag and not for every instances,
adversarial perturbations can be fatal. In this paper, we have proposed two
adversarial perturbation methods to analyze the effect of adversarial
perturbations to interpret the vulnerability of MIL methods. Out of the two
algorithms, one can be customized for every bag, and the other is a universal
one, which can affect all bags in a given data set and thus has some
generalizability. Through simulations, we have also shown the effectiveness of
the proposed algorithms to fool the state-of-the-art (SOTA) MIL methods.
Finally, we have discussed through experiments, about taking care of these kind
of adversarial perturbations through a simple strategy. Source codes are
available at https://github.com/InkiInki/MI-UAP.
