# Towards Crafting Text Adversarial Samples

arXiv: https://arxiv.org/abs/1707.02812 · HTML: https://mlsanity.com/paper/1707.02812/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2017.

## Built on by (most cited first, 30 of 55)

- [Gradient-based Adversarial Attacks against Text Transformers](https://mlsanity.com/paper/2104.13733/) (2021)
- [Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks](https://mlsanity.com/paper/2310.10844/) (2023)
- [Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP](https://mlsanity.com/paper/2210.10683/) (2022)
- [A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://mlsanity.com/paper/2305.11391/) (2023)
- [Certified Robustness to Text Adversarial Attacks by Randomized [MASK]](https://mlsanity.com/paper/2105.03743/) (2021)
- [Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing](https://mlsanity.com/paper/2108.04990/) (2021)
- [A Review of Adversarial Attack and Defense for Classification Methods](https://mlsanity.com/paper/2111.09961/) (2021)
- [A Survey of Adversarial Defences and Robustness in NLP](https://mlsanity.com/paper/2203.06414/) (2022)
- [Evaluating the Robustness of Discrete Prompts](https://mlsanity.com/paper/2302.05619/) (2023)
- [Security and Privacy for Artificial Intelligence: Opportunities and Challenges](https://mlsanity.com/paper/2102.04661/) (2021)
- [On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook](https://mlsanity.com/paper/2307.16680/) (2023)
- [Rapid Optimization for Jailbreaking LLMs via Subconscious Exploitation and Echopraxia](https://mlsanity.com/paper/2402.05467/) (2024)
- [Multi-granular Adversarial Attacks against Black-box Neural Ranking Models](https://mlsanity.com/paper/2404.01574/) (2024)
- [Fall of Giants: How popular text-based MLaaS fall against a simple evasion attack](https://mlsanity.com/paper/2104.05996/) (2021)
- [Token-Modification Adversarial Attacks for Natural Language Processing: A Survey](https://mlsanity.com/paper/2103.00676/) (2021)
- [Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation](https://mlsanity.com/paper/2203.12709/) (2022)
- [Spiking Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/2406.19230/) (2024)
- [A Differentiable Language Model Adversarial Attack on Text Classifiers](https://mlsanity.com/paper/2107.11275/) (2021)
- [A Modified Word Saliency-Based Adversarial Attack on Text Classification Models](https://mlsanity.com/paper/2403.11297/) (2024)
- [Evaluating Document Coherence Modelling](https://mlsanity.com/paper/2103.10133/) (2021)
- [Residue-Based Natural Language Adversarial Attack Detection](https://mlsanity.com/paper/2204.10192/) (2022)
- [Efficient Adversarial Training With Data Pruning](https://mlsanity.com/paper/2207.00694/) (2022)
- [Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features](https://mlsanity.com/paper/2207.04307/) (2022)
- [CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability](https://mlsanity.com/paper/2310.14265/) (2023)
- [Cheating Automatic Short Answer Grading: On the Adversarial Usage of Adjectives and Adverbs](https://mlsanity.com/paper/2201.08318/) (2022)
- [Dynamic Time Warping based Adversarial Framework for Time-Series Domain](https://mlsanity.com/paper/2207.04308/) (2022)
- [ProTransformer: Robustify Transformers via Plug-and-Play Paradigm](https://mlsanity.com/paper/2410.23182/) (2024)
- [EDoG: Adversarial Edge Detection For Graph Neural Networks](https://mlsanity.com/paper/2212.13607/) (2022)
- [TREATED:Towards Universal Defense against Textual Adversarial Attacks](https://mlsanity.com/paper/2109.06176/) (2021)
- [Towards Efficient and Domain-Agnostic Evasion Attack with High-dimensional Categorical Inputs](https://mlsanity.com/paper/2212.06836/) (2022)

## Abstract (arXiv)

Adversarial samples are strategically modified samples, which are crafted with the purpose of fooling a classifier at hand. An attacker introduces specially crafted adversarial samples to a deployed classifier, which are being mis-classified by the classifier. However, the samples are perceived to be drawn from entirely different classes and thus it becomes hard to detect the adversarial samples. Most of the prior works have been focused on synthesizing adversarial samples in the image domain. In this paper, we propose a new method of crafting adversarial text samples by modification of the original samples. Modifications of the original text samples are done by deleting or replacing the important or salient words in the text or by introducing new words in the text sample. Our algorithm works best for the datasets which have sub-categories within each of the classes of examples. While crafting adversarial samples, one of the key constraint is to generate meaningful sentences which can at pass off as legitimate from language (English) viewpoint. Experimental results on IMDB movie review dataset for sentiment analysis and Twitter dataset for gender detection show the efficiency of our proposed method.