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.

在作者验证的敌对设置中，通过与负面合成样本增强分类器训练集，以模仿感兴趣作者的风格，无论是通过隐藏写作风格还是模仿其他作者的风格，本文以三种不同的生成器架构（基于循环神经网络的一种，基于小规模转换器的一种，以及基于流行的 GPT 模型的一种）和两种训练策略（一种受标准语言模型启发，另一种受 Wasserstein 生成对抗网络启发）来分析这种增强方法在作者验证任务中对分类器预测的改进效果。然而，实验结果显示，尽管该方法在许多敌对设置下有效，但其效果在实际应用中过于不稳定。