  Clustering models constitute a class of unsupervised machine learning methods
which are used in a number of application pipelines, and play a vital role in
modern data science. With recent advancements in deep learning -- deep
clustering models have emerged as the current state-of-the-art over traditional
clustering approaches, especially for high-dimensional image datasets. While
traditional clustering approaches have been analyzed from a robustness
perspective, no prior work has investigated adversarial attacks and robustness
for deep clustering models in a principled manner. To bridge this gap, we
propose a blackbox attack using Generative Adversarial Networks (GANs) where
the adversary does not know which deep clustering model is being used, but can
query it for outputs. We analyze our attack against multiple state-of-the-art
deep clustering models and real-world datasets, and find that it is highly
successful. We then employ some natural unsupervised defense approaches, but
find that these are unable to mitigate our attack. Finally, we attack Face++, a
production-level face clustering API service, and find that we can
significantly reduce its performance as well. Through this work, we thus aim to
motivate the need for truly robust deep clustering models.
