{"total":85,"rows":[{"id":"2112.02721","title":"NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation","created":"2021-12-06","cited_by_count":73,"area":null},{"id":"2107.13541","title":"Towards Robustness Against Natural Language Word Substitutions","created":"2021-07-28","cited_by_count":69,"area":null},{"id":"2105.03743","title":"Certified Robustness to Text Adversarial Attacks by Randomized [MASK]","created":"2021-05-08","cited_by_count":57,"area":null},{"id":"2112.11668","title":"How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?","created":"2021-12-22","cited_by_count":49,"area":null},{"id":"2104.08211","title":"Robust Open-Vocabulary Translation from Visual Text Representations","created":"2021-04-16","cited_by_count":47,"area":null},{"id":"2301.12868","title":"On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex","created":"2023-01-30","cited_by_count":41,"area":null},{"id":"2304.08979","title":"In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT","created":"2023-04-18","cited_by_count":37,"area":null},{"id":"2309.14122","title":"SurrogatePrompt: Bypassing the Safety Filter of Text-to-Image Models via Substitution","created":"2023-09-25","cited_by_count":34,"area":null},{"id":"2305.10235","title":"Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility","created":"2023-05-15","cited_by_count":30,"area":null},{"id":"2402.11638","title":"Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks","created":"2024-02-18","cited_by_count":26,"area":null},{"id":"2110.04399","title":"Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors","created":"2021-10-08","cited_by_count":25,"area":null},{"id":"2204.04636","title":"\"That Is a Suspicious Reaction!\": Interpreting Logits Variation to Detect NLP Adversarial Attacks","created":"2022-04-10","cited_by_count":24,"area":null},{"id":"2205.12331","title":"Certified Robustness Against Natural Language Attacks by Causal Intervention","created":"2022-05-24","cited_by_count":24,"area":null},{"id":"2302.05706","title":"MTTM: Metamorphic Testing for Textual Content Moderation Software","created":"2023-02-11","cited_by_count":22,"area":null},{"id":"2109.04367","title":"Multi-granularity Textual Adversarial Attack with Behavior Cloning","created":"2021-09-09","cited_by_count":21,"area":null},{"id":"2106.01452","title":"BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks","created":"2021-06-02","cited_by_count":17,"area":null},{"id":"2109.02229","title":"Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack","created":"2021-09-06","cited_by_count":17,"area":null},{"id":"2110.07159","title":"Interpreting the Robustness of Neural NLP Models to Textual Perturbations","created":"2021-10-14","cited_by_count":17,"area":null},{"id":"2401.03321","title":"PIXAR: Auto-Regressive Language Modeling in Pixel Space","created":"2024-01-06","cited_by_count":16,"area":null},{"id":"2107.11252","title":"Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation","created":"2021-07-23","cited_by_count":15,"area":null},{"id":"2203.10378","title":"On Robust Prefix-Tuning for Text Classification","created":"2022-03-19","cited_by_count":15,"area":null},{"id":"2203.11131","title":"Towards Explainable Evaluation Metrics for Natural Language Generation","created":"2022-03-21","cited_by_count":15,"area":null},{"id":"2101.11310","title":"Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling","created":"2021-01-27","cited_by_count":13,"area":null},{"id":"2203.10346","title":"Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense","created":"2022-03-19","cited_by_count":13,"area":null},{"id":"2502.15343","title":"Tokenization is Sensitive to Language Variation","created":"2025-02-21","cited_by_count":13,"area":null},{"id":"2103.00676","title":"Token-Modification Adversarial Attacks for Natural Language Processing: A Survey","created":"2021-03-01","cited_by_count":12,"area":null},{"id":"2311.11861","title":"Generating Valid and Natural Adversarial Examples with Large Language Models","created":"2023-11-20","cited_by_count":12,"area":null},{"id":"2201.00768","title":"Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions","created":"2022-01-03","cited_by_count":11,"area":null},{"id":"2205.00034","title":"What do we Really Know about State of the Art NER?","created":"2022-04-29","cited_by_count":10,"area":null},{"id":"2209.02317","title":"Layer or Representation Space: What makes BERT-based Evaluation Metrics Robust?","created":"2022-09-06","cited_by_count":10,"area":null},{"id":"2107.11275","title":"A Differentiable Language Model Adversarial Attack on Text Classifiers","created":"2021-07-23","cited_by_count":9,"area":null},{"id":"2510.18840","title":"See the Text: From Tokenization to Visual Reading","created":"2025-10-21","cited_by_count":9,"area":null},{"id":"2203.11331","title":"On The Robustness of Offensive Language Classifiers","created":"2022-03-21","cited_by_count":8,"area":null},{"id":"2302.02023","title":"TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification","created":"2023-02-03","cited_by_count":8,"area":null},{"id":"2303.08032","title":"Verifying the Robustness of Automatic Credibility Assessment","created":"2023-03-14","cited_by_count":8,"area":null},{"id":"2303.14443","title":"No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning","created":"2023-03-25","cited_by_count":7,"area":null},{"id":"2301.06494","title":"CRYPTEXT: Database and Interactive Toolkit of Human-Written Text Perturbations in the Wild","created":"2023-01-16","cited_by_count":6,"area":null},{"id":"2308.11284","title":"LEAP: Efficient and Automated Test Method for NLP Software","created":"2023-08-22","cited_by_count":6,"area":null},{"id":"2310.14265","title":"CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability","created":"2023-10-22","cited_by_count":6,"area":null},{"id":"2111.01528","title":"Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization","created":"2021-11-02","cited_by_count":5,"area":null},{"id":"2112.12792","title":"Understanding and Measuring Robustness of Multimodal Learning","created":"2021-12-22","cited_by_count":5,"area":null},{"id":"2201.06384","title":"Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations","created":"2022-01-17","cited_by_count":5,"area":null},{"id":"2202.09483","title":"Data-Driven Mitigation of Adversarial Text Perturbation","created":"2022-02-19","cited_by_count":5,"area":null},{"id":"2206.04137","title":"Adversarial Text Normalization","created":"2022-06-08","cited_by_count":5,"area":null},{"id":"2210.13749","title":"AugCSE: Contrastive Sentence Embedding with Diverse Augmentations","created":"2022-10-20","cited_by_count":5,"area":null},{"id":"2304.08767","title":"Masked Language Model Based Textual Adversarial Example Detection","created":"2023-04-18","cited_by_count":5,"area":null},{"id":"2502.04643","title":"Confidence Elicitation: A New Attack Vector for Large Language Models","created":"2025-02-07","cited_by_count":5,"area":null},{"id":"2110.13980","title":"Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions","created":"2021-10-26","cited_by_count":4,"area":null},{"id":"2201.08555","title":"Identifying Adversarial Attacks on Text Classifiers","created":"2022-01-21","cited_by_count":4,"area":null},{"id":"2210.07595","title":"The State of Profanity Obfuscation in Natural Language Processing","created":"2022-10-14","cited_by_count":4,"area":null},{"id":"2412.02323","title":"Pay Attention to the Robustness of Chinese Minority Language Models! Syllable-level Textual Adversarial Attack on Tibetan Script","created":"2024-12-03","cited_by_count":4,"area":null},{"id":"2505.17994","title":"Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation","created":"2025-05-23","cited_by_count":4,"area":null},{"id":"2205.01714","title":"Don't sweat the small stuff, classify the rest: Sample Shielding to protect text classifiers against adversarial attacks","created":"2022-05-03","cited_by_count":3,"area":null},{"id":"2303.05077","title":"Learning the Legibility of Visual Text Perturbations","created":"2023-03-09","cited_by_count":3,"area":null},{"id":"2305.18503","title":"From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework","created":"2023-05-29","cited_by_count":3,"area":null},{"id":"2402.13006","title":"Investigating the Impact of Model Instability on Explanations and Uncertainty","created":"2024-02-20","cited_by_count":3,"area":null},{"id":"2403.18423","title":"SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks","created":"2024-03-27","cited_by_count":3,"area":null},{"id":"2409.18708","title":"Evading Toxicity Detection with ASCII-art: A Benchmark of Spatial Attacks on Moderation Systems","created":"2024-09-27","cited_by_count":3,"area":null},{"id":"2101.08675","title":"Adversarial Machine Learning in Text Analysis and Generation","created":"2021-01-14","cited_by_count":2,"area":null},{"id":"2210.12233","title":"TCAB: A Large-Scale Text Classification Attack Benchmark","created":"2022-10-21","cited_by_count":2,"area":null},{"id":"2403.05365","title":"The Impact of Quantization on the Robustness of Transformer-based Text Classifiers","created":"2024-03-08","cited_by_count":2,"area":null},{"id":"2412.02371","title":"TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual Similarity","created":"2024-12-03","cited_by_count":2,"area":null},{"id":"2412.20087","title":"On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs","created":"2024-12-28","cited_by_count":2,"area":null},{"id":"2501.02654","title":"Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks","created":"2025-01-05","cited_by_count":2,"area":null},{"id":"2502.05225","title":"BitAbuse: A Dataset of Visually Perturbed Texts for Defending Phishing Attacks","created":"2025-02-06","cited_by_count":2,"area":null},{"id":"2212.11808","title":"A Mutation-based Text Generation for Adversarial Machine Learning Applications","created":"2022-12-21","cited_by_count":1,"area":null},{"id":"2308.10201","title":"Hiding Backdoors within Event Sequence Data via Poisoning Attacks","created":"2023-08-20","cited_by_count":1,"area":null},{"id":"2310.01452","title":"Fooling the Textual Fooler via Randomizing Latent Representations","created":"2023-10-02","cited_by_count":1,"area":null},{"id":"2404.08538","title":"VertAttack: Taking advantage of Text Classifiers' horizontal vision","created":"2024-04-12","cited_by_count":1,"area":null},{"id":"2412.02343","title":"Multi-Granularity Tibetan Textual Adversarial Attack Method Based on Masked Language Model","created":"2024-12-03","cited_by_count":1,"area":null},{"id":"2412.10617","title":"BinarySelect to Improve Accessibility of Black-Box Attack Research","created":"2024-12-13","cited_by_count":1,"area":null},{"id":"2205.12514","title":"Machine Translation Robustness to Natural Asemantic Variation","created":"2022-05-25","cited_by_count":0,"area":null},{"id":"2207.02639","title":"Adversarial Robustness of Visual Dialog","created":"2022-07-06","cited_by_count":0,"area":null},{"id":"2208.09915","title":"MockingBERT: A Method for Retroactively Adding Resilience to NLP Models","created":"2022-08-21","cited_by_count":0,"area":null},{"id":"2211.04973","title":"Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation","created":"2022-11-09","cited_by_count":0,"area":null},{"id":"2307.01488","title":"SCAT: Robust Self-supervised Contrastive Learning via Adversarial Training for Text Classification","created":"2023-07-04","cited_by_count":0,"area":null},{"id":"2311.17722","title":"SenTest: Evaluating Robustness of Sentence Encoders","created":"2023-11-29","cited_by_count":0,"area":null},{"id":"2411.07850","title":"IAE: Irony-based Adversarial Examples for Sentiment Analysis Systems","created":"2024-11-12","cited_by_count":0,"area":null},{"id":"2412.10805","title":"Are Language Models Agnostic to Linguistically Grounded Perturbations? A Case Study of Indic Languages","created":"2024-12-14","cited_by_count":0,"area":null},{"id":"2508.21206","title":"Enhancing Robustness of Autoregressive Language Models against Orthographic Attacks via Pixel-based Approach","created":"2025-08-28","cited_by_count":0,"area":null},{"id":"2509.20691","title":"RedHerring Attack: Testing the Reliability of Attack Detection","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2509.20699","title":"Overcoming Black-box Attack Inefficiency with Hybrid and Dynamic Select Algorithms","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2511.12423","title":"GRAPHTEXTACK: A Realistic Black-Box Node Injection Attack on LLM-Enhanced GNNs","created":"2025-11-16","cited_by_count":0,"area":null},{"id":"2606.09700","title":"What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2608.03842","title":"Sensitivity, Causality, and Repair Dissociate: A Layer-Wise Analysis of Perturbation Robustness and Its Scaling","created":"2026-08-04","cited_by_count":0,"area":null}],"next_cursor":null}