Adversarial attacks reveal important vulnerabilities and flaws of trained
models. One potent type of attack are universal adversarial triggers, which are
individual n-grams that, when appended to instances of a class under attack,
can trick a model into predicting a target class. However, for inference tasks
such as fact checking, these triggers often inadvertently invert the meaning of
instances they are inserted in. In addition, such attacks produce semantically
nonsensical inputs, as they simply concatenate triggers to existing samples.
Here, we investigate how to generate adversarial attacks against fact checking
systems that preserve the ground truth meaning and are semantically valid. We
extend the HotFlip attack algorithm used for universal trigger generation by
jointly minimising the target class loss of a fact checking model and the
entailment class loss of an auxiliary natural language inference model. We then
train a conditional language model to generate semantically valid statements,
which include the found universal triggers. We find that the generated attacks
maintain the directionality and semantic validity of the claim better than
previous work.

本文主要研究如何生成对于事实核查系统具有对抗性的攻击，使其保持着地面事实的意义和语义的有效性，为此采用了 HotFlip 攻击算法与条件语言模型相结合的方法，生成出了一批具有方向性和语义有效性的攻击。