SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial Text
This work addresses adversarial robustness in text classification, offering a method to detect and defend against attacks, but it is incremental as it builds on ensemble-based approaches.
The paper tackles the problem of defending against adversarial text attacks by exploiting differences in predicted probabilities between a victim classifier and other classifiers, proposing SEPP to correct misclassifications and demonstrating its resilience across various classifiers, tasks, and attacks.
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.