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

arXiv: https://arxiv.org/abs/2110.05748 · HTML: https://mlsanity.com/paper/2110.05748/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 38 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [Universal Adversarial Triggers for Attacking and Analyzing NLP](https://mlsanity.com/paper/1908.07125/) (2019)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Adversarial Examples for Evaluating Reading Comprehension Systems](https://mlsanity.com/paper/1707.07328/) (2017)
- [BERT-ATTACK: Adversarial Attack Against BERT Using BERT](https://mlsanity.com/paper/2004.09984/) (2020)
- [Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/1804.07998/) (2018)
- [TextBugger: Generating Adversarial Text Against Real-world Applications](https://mlsanity.com/paper/1812.05271/) (2018)
- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [BAE: BERT-based Adversarial Examples for Text Classification](https://mlsanity.com/paper/2004.01970/) (2020)
- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://mlsanity.com/paper/1804.06059/) (2018)
- [Certified Robustness to Adversarial Word Substitutions](https://mlsanity.com/paper/1909.00986/) (2019)
- [Combating Adversarial Misspellings with Robust Word Recognition](https://mlsanity.com/paper/1905.11268/) (2019)
- [Deep Text Classification Can be Fooled](https://mlsanity.com/paper/1704.08006/) (2017)
- [Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation](https://mlsanity.com/paper/1909.01492/) (2019)
- [SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions](https://mlsanity.com/paper/2005.14424/) (2020)
- [Robust Encodings: A Framework for Combating Adversarial Typos](https://mlsanity.com/paper/2005.01229/) (2020)
- [Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification](https://mlsanity.com/paper/1909.03084/) (2019)
- [Generating Fluent Adversarial Examples for Natural Languages](https://mlsanity.com/paper/2007.06174/) (2020)
- [CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation](https://mlsanity.com/paper/2010.02338/) (2020)
- [Word-level Textual Adversarial Attacking as Combinatorial Optimization](https://mlsanity.com/paper/1910.12196/) (2019)
- [A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/2010.01345/) (2020)
- [Adversarial Attack and Defense of Structured Prediction Models](https://mlsanity.com/paper/2010.01610/) (2020)
- [Generating Natural Language Adversarial Examples on a Large Scale with Generative Models](https://mlsanity.com/paper/2003.10388/) (2020)
- [White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks](https://mlsanity.com/paper/1904.02405/) (2019)

## Abstract (arXiv)

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.