Papers with adversarial attack methods
Text Adversarial Purification as Defense against Adversarial Attacks (2023.acl-long)
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| Challenge: | Existing methods for defending against adversarial examples are difficult due to the discrete nature of texts. |
| Approach: | They propose a novel adversarial purification method that aims to remove adversarials and make correct predictions based on the recovered clean samples. |
| Outcome: | The proposed method can defend against word-substitution adversarial attacks using language models. |
SafeScientist: Enhancing AI Scientist Safety for Risk-Aware Scientific Discovery (2025.emnlp-main)
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Kunlun Zhu, Jiaxun Zhang, Ziheng Qi, Nuoxing Shang, Zijia Liu, Peixuan Han, Yue Su, Haofei Yu, Jiaxuan You
| Challenge: | Recent advances in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet raised critical ethical and safety concerns. |
| Approach: | They propose a framework to enhance safety and ethical responsibility in AI-driven scientific exploration. |
| Outcome: | The proposed framework significantly improves safety performance by 35% compared to traditional frameworks. |
Evaluating the Validity of Word-level Adversarial Attacks with Large Language Models (2024.findings-acl)
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| 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. |
Adversarial Text Generation by Search and Learning (2023.findings-emnlp)
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Guoyi Li, Bingkang Shi, Zongzhen Liu, Dehan Kong, Yulei Wu, Xiaodan Zhang, Longtao Huang, Honglei Lyu
| Challenge: | Existing text generation methods only use heuristic replacement strategies or language models to generate replacement words at the word level. |
| Approach: | They propose a search and learning framework for Adversarial Text Generation by Search and Learning to evaluate the robustness of natural language processing models. |
| Outcome: | The proposed methods are significantly superior to the most advanced methods in terms of attack efficiency and adversarial text quality. |