A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples (2020.coling-main)
Copied to clipboard
| Challenge: | Existing techniques to generate adversarial examples for natural language are limited . previous research on adversarials focused on images, but this is not the case with natural language. |
| Approach: | They propose a geometry-inspired attack for generating natural language adversarial examples . they use deep neural networks to iteratively approximate the decision boundary . |
| Outcome: | The proposed attack fools natural language models with high success rates while replacing a few words. |
Similar Papers
Generating Natural Language Adversarial Examples (D18-1)
Copied to clipboard
| Challenge: | Recent research has shown that deep neural networks are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. |
| Approach: | They propose to generate adversarial examples that fool well-trained sentiment analysis and textual entailment models by using a black-box population-based optimization algorithm. |
| Outcome: | The proposed model is able to fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively. |
NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries? (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work has explored adversarial example generation for natural language understanding tasks, but these examples are often unrealistic and diverge from the real-world data distributions. |
| Approach: | They propose a framework for adversarial example generation that is effective at fooling a given classifier and a generative model based on the key tokens from the first stage. |
| Outcome: | The proposed framework generalizes across domains and offers insights for future research on improving robustness of neural text classification models. |
Generating Fluent Adversarial Examples for Natural Languages (P19-1)
Copied to clipboard
| Challenge: | Current methods for building adversarial attackers for NLP are inefficient as the gradient is discarded. |
| Approach: | They propose an adversarial attacker which performs Metropolis-Hastings sampling with the guidance of gradients to solve these problems. |
| Outcome: | The proposed algorithm outperforms the baseline model on attacking capability on IMDB and SNLI. |
A Reinforced Generation of Adversarial Examples for Neural Machine Translation (2020.acl-main)
Copied to clipboard
| Challenge: | Neural machine translation systems fail on less decent inputs, which may harm the credibility of these systems. |
| Approach: | They propose a paradigm that generates adversarial examples using reinforcement learning to expose pitfalls for a given performance metric. |
| Outcome: | The proposed paradigm produces stable attacks with meaning-preserving adversarial examples. |
Reevaluating Adversarial Examples in Natural Language (2020.findings-emnlp)
Copied to clipboard
| Challenge: | State-of-the-art adversarial examples lack a common definition of what constitutes success . human surveys show that to preserve semantics, we need to increase the minimum cosine similarities between the embeddings of swapped words and between the sentence encodings of original and perturbed sentences. |
| Approach: | They propose a unified definition of what constitutes a successful adversarial example . they propose four categories of constraints that are used to define adversarials . |
| Outcome: | The proposed framework is based on the outputs of two state-of-the-art synonym substitution attacks. |
Evaluating the Validity of Word-level Adversarial Attacks with Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing adversarial examples can generate invalid adversarials due to significant changes in semantic meanings compared to their originals. |
| Approach: | They propose to use a large language model to evaluate adversarial examples by semantic constraints. |
| Outcome: | The proposed method can generate valid adversarial examples even when they are not equipped with semantic constraints. |
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent work has raised the question of whether valid adversarial inputs are feasible. |
| Approach: | They analyze how human-generated adversarial examples compare to the best algorithms . they use crowdsourcing to modify words in an input text with immediate feedback . |
| Outcome: | The proposed algorithms are not more efficient than the best to generate natural-reading, sentiment-preserving examples. |
Identifying Human Strategies for Generating Word-Level Adversarial Examples (2022.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study shows that word-level adversarial examples are more natural and grammatical correct than automated attacks. |
| Approach: | They analyze how humans generate word-level adversarial examples against fine-tuned Transformer models that preserve naturalness and grammatical correctness. |
| Outcome: | The authors show that humans generate adversarial examples much more effortlessly than automated attacks. |
Using Adversarial Examples in Natural Language Processing (L18-1)
Copied to clipboard
| Challenge: | Recent advances in machine learning have led to the use of adversarial examples in training of neural networks. |
| Approach: | They investigate the effect of using adversarial examples during training of recurrent neural networks whose text input is in the form of a sequence of word/character embeddings. |
| Outcome: | The proposed method provides regularization effect and enables training of models with greater number of parameters without overfitting. |
Adversarial Examples for Evaluating Math Word Problem Solvers (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing MWP solvers do not understand language and its relation with numbers, and their accuracy is unclear. |
| Approach: | They propose two methods to generate adversarial attacks to evaluate the robustness of existing MWP solvers. |
| Outcome: | The proposed method reduces the accuracy of existing MWP solvers by over 40% on two benchmark datasets. |