There are two cases describing how a classifier processes input text, namely,
misclassification and correct classification. In terms of misclassified texts,
a classifier handles the texts with both incorrect predictions and adversarial
texts, which are generated to fool the classifier, which is called a victim.
Both types are misunderstood by the victim, but they can still be recognized by
other classifiers. This induces large gaps in predicted probabilities between
the victim and the other classifiers. In contrast, text correctly classified by
the victim is often successfully predicted by the others and induces small
gaps. In this paper, we propose an ensemble model based on similarity
estimation of predicted probabilities (SEPP) to exploit the large gaps in the
misclassified predictions in contrast to small gaps in the correct
classification. SEPP then corrects the incorrect predictions of the
misclassified texts. We demonstrate the resilience of SEPP in defending and
detecting adversarial texts through different types of victim classifiers,
classification tasks, and adversarial attacks.

该论文提出了一种基于相似度估计的预测概率的集成模型 (SEPP)，用于纠正分类器对误分类文本的不正确预测，并展示了该模型在不同类型的受害者分类器、分类任务和对抗攻击中识别和捍卫信息安全的鲁棒性。