Forging the Forger: An Attempt to Improve Authorship Verification via
  Data Augmentation
Silvia Corbara Alejandro Moreo
Authorship Verification (AV) is a text classification task concerned with
inferring whether a candidate text has been written by one specific author or
by someone else. It has been shown that many AV systems are vulnerable to
adversarial attacks, where a malicious author actively tries to fool the
classifier by either concealing their writing style, or by imitating the style
of another author. In this paper, we investigate the potential benefits of
augmenting the classifier training set with (negative) synthetic examples.
These synthetic examples are generated to imitate the style of the author of
interest. We analyze the improvements in classifier prediction that this
augmentation brings to bear in the task of AV in an adversarial setting. In
particular, we experiment with three different generator architectures (one
based on Recurrent Neural Networks, another based on small-scale transformers,
and another based on the popular GPT model) and with two training strategies
(one inspired by standard Language Models, and another inspired by Wasserstein
Generative Adversarial Networks). We evaluate our hypothesis on five datasets
(three of which have been specifically collected to represent an adversarial
setting) and using two learning algorithms for the AV classifier (Support
Vector Machines and Convolutional Neural Networks). This experimentation has
yielded negative results, revealing that, although our methodology proves
effective in many adversarial settings, its benefits are too sporadic for a
pragmatical application.