Sentence-level attacks craft adversarial sentences that are synonymous with
correctly-classified sentences but are misclassified by the text classifiers.
Under the black-box setting, classifiers are only accessible through their
feedback to queried inputs, which is predominately available in the form of
class probabilities. Even though utilizing class probabilities results in
stronger attacks, due to the challenges of using them for sentence-level
attacks, existing attacks use either no feedback or only the class labels.
Overcoming the challenges, we develop a novel algorithm that uses class
probabilities for black-box sentence-level attacks, investigate the
effectiveness of using class probabilities on the attack's success, and examine
the question if it is worthy or practical to use class probabilities by
black-box sentence-level attacks. We conduct extensive evaluations of the
proposed attack comparing with the baselines across various classifiers and
benchmark datasets.

使用类概率以进行黑盒子句级攻击的新算法，通过与基准模型和基准数据集的比较进行全面评估，研究了类概率在攻击成功方面的有效性，并探讨了在黑盒子句级攻击中使用类概率是否值得或实用。