Title: Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models,
Abstract: Machine Learning (ML) and Deep Learning (DL) models have achieved
state-of-the-art performance on multiple learning tasks, from vision to natural
language modelling. With the growing adoption of ML and DL to many areas of
computer science, recent research has also started focusing on the security
properties of these models. There has been a lot of work undertaken to
understand if (deep) neural network architectures are resilient to black-box
adversarial attacks which craft perturbed input samples that fool the
classifier without knowing the architecture used. Recent work has also focused
on the transferability of adversarial attacks and found that adversarial
attacks are generally easily transferable between models, datasets, and
techniques. However, such attacks and their analysis have not been covered from
the perspective of unsupervised machine learning algorithms. In this paper, we
seek to bridge this gap through multiple contributions. We first provide a
strong (iterative) black-box adversarial attack that can craft adversarial
samples which will be incorrectly clustered irrespective of the choice of
clustering algorithm. We choose 4 prominent clustering algorithms, and a
real-world dataset to show the working of the proposed adversarial algorithm.
Using these clustering algorithms we also carry out a simple study of
cross-technique adversarial attack transferability.