SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial Text

TL;DR

This paper introduces SEPP, an ensemble method that estimates similarity of predicted probabilities to detect and defend against adversarial texts by exploiting probability gap patterns.

Contribution

SEPP is a novel ensemble approach that leverages probability similarity estimation to improve adversarial text detection and correction.

Findings

SEPP effectively detects adversarial texts across multiple classifiers.

SEPP improves classification accuracy on adversarial examples.

SEPP demonstrates robustness against various attack types.

Abstract

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.