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Adversarial attacks on text classification models using layer‐wise relevance propagation

https://doi.org/10.1002/int.22260
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1038/323533a0openalex-snapshot
Learning representations by back-propagating errors
inferredcites →https://doi.org/10.1109/cvpr.2016.318openalex-snapshot
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
inferredcites →https://doi.org/10.1109/maes.2007.327521openalex-snapshot
The Significance of Letter Position in Word Recognition
inferredcites →https://doi.org/10.1109/milcom.2016.7795300openalex-snapshot
Crafting adversarial input sequences for recurrent neural networks
inferredcites →https://doi.org/10.1109/spw.2018.00016openalex-snapshot
Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
inferredcites →https://doi.org/10.1109/tnnls.2016.2599820openalex-snapshot
Evaluating the Visualization of What a Deep Neural Network Has Learned
inferredcites →https://doi.org/10.1111/j.1467-9280.2006.01684.xopenalex-snapshot
Raeding Wrods With Jubmled Lettres
inferredcites →https://doi.org/10.1145/1273496openalex-snapshot
Proceedings of the 24th international conference on Machine learning
inferredcites →https://doi.org/10.1162/coli_a_00300openalex-snapshot
Representation of Linguistic Form and Function in Recurrent Neural Networks
inferredcites →https://doi.org/10.1371/journal.pone.0130140openalex-snapshot
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
inferredcites →https://doi.org/10.14722/ndss.2019.23138openalex-snapshot
TextBugger: Generating Adversarial Text Against Real-world Applications
inferredcites →https://doi.org/10.18653/v1/d18-1316openalex-snapshot
Generating Natural Language Adversarial Examples
inferredcites →https://doi.org/10.18653/v1/n16-1082openalex-snapshot
Visualizing and Understanding Neural Models in NLP
inferredcites →https://doi.org/10.18653/v1/n18-1097openalex-snapshot
Comparing Automatic and Human Evaluation of Local Explanations for Text Classification
inferredcites →https://doi.org/10.18653/v1/n18-1170openalex-snapshot
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
inferredcites →https://doi.org/10.18653/v1/p17-1106openalex-snapshot
Visualizing and Understanding Neural Machine Translation
inferredcites →https://doi.org/10.18653/v1/p18-1079openalex-snapshot
Semantically Equivalent Adversarial Rules for Debugging NLP models
inferredcites →https://doi.org/10.18653/v1/p19-1103openalex-snapshot
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency
inferredcites →https://doi.org/10.18653/v1/p19-1559openalex-snapshot
Generating Fluent Adversarial Examples for Natural Languages
inferredcites →https://doi.org/10.18653/v1/w16-1601openalex-snapshot
Explaining Predictions of Non-Linear Classifiers in NLP
inferredcites →https://doi.org/10.18653/v1/w17-5221openalex-snapshot
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
inferredcites →https://doi.org/10.24963/ijcai.2018/585openalex-snapshot
Deep Text Classification Can be Fooled
inferredcites →https://doi.org/10.24963/ijcai.2018/601openalex-snapshot
Interpretable Adversarial Perturbation in Input Embedding Space for Text
inferredcites →https://doi.org/10.3115/v1/d14-1181openalex-snapshot
Convolutional Neural Networks for Sentence Classification
inferredcites →https://doi.org/10.3115/v1/e14-1openalex-snapshot
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics
inferredcites →https://doi.org/10.48550/arxiv.0912.1128openalex-snapshot
How to Explain Individual Classification Decisions

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Extended variational inference for Dirichlet process mixture of Beta‐Liouville distributions for proportional data modeling
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A data‐driven adversarial examples recognition framework via adversarial feature genomes
inferred← citeshttps://doi.org/10.1002/int.22851openalex-snapshot
D etect S ec : Evaluating the robustness of object detection models to adversarial attacks
inferred← citeshttps://doi.org/10.1002/widm.70075openalex-snapshot
Interpretable Machine Learning: A Comprehensive Review of Foundations, Methods, and the Path Forward
inferred← citeshttps://doi.org/10.1007/s10994-024-06572-5openalex-snapshot
Kalt: generating adversarial explainable chinese legal texts
inferred← citeshttps://doi.org/10.1007/s11280-025-01338-yopenalex-snapshot
Chinese legal adversarial text generation based on interpretable perturbation strategies
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Explanation of deep learning models
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111008openalex-snapshot
EVolutionary independent DEtermiNistiC explanation
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.119110openalex-snapshot
Aliasing black box adversarial attack with joint self-attention distribution and confidence probability
inferred← citeshttps://doi.org/10.1016/j.neunet.2025.108262openalex-snapshot
AdaptiveWordBug: Generating adversarial texts with an adaptive scoring strategy against deep learning classifiers
inferred← citeshttps://doi.org/10.1108/bij-07-2022-0454openalex-snapshot
Text classification using deep learning techniques: a bibliometric analysis and future research directions
inferred← citeshttps://doi.org/10.1109/tdsc.2022.3143566openalex-snapshot
Attacking Deep Reinforcement Learning With Decoupled Adversarial Policy
inferred← citeshttps://doi.org/10.1155/2022/3576419openalex-snapshot
Design of Shared Internet of Things System for English Translation Teaching Using Deep Learning Text Classification
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