  Adversarial attacks on machine learning algorithms have been a key deterrent
to the adoption of AI in many real-world use cases. They significantly
undermine the ability of high-performance neural networks by forcing
misclassifications. These attacks introduce minute and structured perturbations
or alterations in the test samples, imperceptible to human annotators in
general, but trained neural networks and other models are sensitive to it.
Historically, adversarial attacks have been first identified and studied in the
domain of image processing. In this paper, we study adversarial examples in the
field of natural language processing, specifically text classification tasks.
We investigate the reasons for adversarial vulnerability, particularly in
relation to the inherent dimensionality of the model. Our key finding is that
there is a very strong correlation between the embedding dimensionality of the
adversarial samples and their effectiveness on models tuned with input samples
with same embedding dimension. We utilize this sensitivity to design an
adversarial defense mechanism. We use ensemble models of varying inherent
dimensionality to thwart the attacks. This is tested on multiple datasets for
its efficacy in providing robustness. We also study the problem of measuring
adversarial perturbation using different distance metrics. For all of the
aforementioned studies, we have run tests on multiple models with varying
dimensionality and used a word-vector level adversarial attack to substantiate
the findings.
