Saved in:
| Main Authors: | Wang, Yue, Wei, Liesheng, Wang, Yuxiang |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.11933 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
T-Detect: Tail-Aware Statistical Normalization for Robust Detection of Adversarial Machine-Generated Text
by: West, Alva, et al.
Published: (2025)
by: West, Alva, et al.
Published: (2025)
MAGE: Machine-generated Text Detection in the Wild
by: Li, Yafu, et al.
Published: (2023)
by: Li, Yafu, et al.
Published: (2023)
LM$^2$otifs : An Explainable Framework for Machine-Generated Texts Detection
by: Zheng, Xu, et al.
Published: (2025)
by: Zheng, Xu, et al.
Published: (2025)
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection
by: Yang, Tiankai, et al.
Published: (2025)
by: Yang, Tiankai, et al.
Published: (2025)
ACE-Sync: An Adaptive Cloud-Edge Synchronization Framework for Communication-Efficient Large-Scale Distributed Model Training
by: Yang, Yi, et al.
Published: (2025)
by: Yang, Yi, et al.
Published: (2025)
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
by: Wang, Bing, et al.
Published: (2023)
by: Wang, Bing, et al.
Published: (2023)
DAMAGE: Detecting Adversarially Modified AI Generated Text
by: Masrour, Elyas, et al.
Published: (2025)
by: Masrour, Elyas, et al.
Published: (2025)
IMGTB: A Framework for Machine-Generated Text Detection Benchmarking
by: Spiegel, Michal, et al.
Published: (2023)
by: Spiegel, Michal, et al.
Published: (2023)
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection
by: Wang, Yuxia, et al.
Published: (2023)
by: Wang, Yuxia, et al.
Published: (2023)
Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial Training
by: Li, Yuanfan, et al.
Published: (2025)
by: Li, Yuanfan, et al.
Published: (2025)
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack
by: Zhou, Ying, et al.
Published: (2024)
by: Zhou, Ying, et al.
Published: (2024)
Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection
by: Wu, Chenwang, et al.
Published: (2026)
by: Wu, Chenwang, et al.
Published: (2026)
Stochastic Adversarial Networks for Multi-Domain Text Classification
by: Wang, Xu, et al.
Published: (2024)
by: Wang, Xu, et al.
Published: (2024)
MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts
by: Macko, Dominik, et al.
Published: (2024)
by: Macko, Dominik, et al.
Published: (2024)
From Insight to Exploit: Leveraging LLM Collaboration for Adaptive Adversarial Text Generation
by: Sultana, Najrin, et al.
Published: (2025)
by: Sultana, Najrin, et al.
Published: (2025)
Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature
by: Bao, Guangsheng, et al.
Published: (2023)
by: Bao, Guangsheng, et al.
Published: (2023)
Exploring the Limitations of Detecting Machine-Generated Text
by: Doughman, Jad, et al.
Published: (2024)
by: Doughman, Jad, et al.
Published: (2024)
Fight Poison with Poison: Enhancing Robustness in Few-shot Machine-Generated Text Detection with Adversarial Training
by: Duan, Wenjing, et al.
Published: (2026)
by: Duan, Wenjing, et al.
Published: (2026)
Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Framework
by: Tao, Zhen, et al.
Published: (2024)
by: Tao, Zhen, et al.
Published: (2024)
Collaborating Action by Action: A Multi-agent LLM Framework for Embodied Reasoning
by: White, Isadora, et al.
Published: (2025)
by: White, Isadora, et al.
Published: (2025)
QuadSentinel: Sequent Safety for Machine-Checkable Control in Multi-agent Systems
by: Yang, Yiliu, et al.
Published: (2025)
by: Yang, Yiliu, et al.
Published: (2025)
MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents
by: Zhu, Kunlun, et al.
Published: (2025)
by: Zhu, Kunlun, et al.
Published: (2025)
AgentCTG: Harnessing Multi-Agent Collaboration for Fine-Grained Precise Control in Text Generation
by: Zhou, Xinxu, et al.
Published: (2025)
by: Zhou, Xinxu, et al.
Published: (2025)
SegHist: A General Segmentation-based Framework for Chinese Historical Document Text Line Detection
by: Hu, Xingjian, et al.
Published: (2024)
by: Hu, Xingjian, et al.
Published: (2024)
Detecting the Machine: A Comprehensive Benchmark of AI-Generated Text Detectors Across Architectures, Domains, and Adversarial Conditions
by: Baidya, Madhav S., et al.
Published: (2026)
by: Baidya, Madhav S., et al.
Published: (2026)
Authorship Obfuscation in Multilingual Machine-Generated Text Detection
by: Macko, Dominik, et al.
Published: (2024)
by: Macko, Dominik, et al.
Published: (2024)
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator
by: Zhu, Junda, et al.
Published: (2024)
by: Zhu, Junda, et al.
Published: (2024)
LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?
by: Zhang, Qihui, et al.
Published: (2024)
by: Zhang, Qihui, et al.
Published: (2024)
When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detection
by: Gao, Lang, et al.
Published: (2025)
by: Gao, Lang, et al.
Published: (2025)
Detection of Machine-Generated Text: Literature Survey
by: Valiaiev, Dmytro
Published: (2024)
by: Valiaiev, Dmytro
Published: (2024)
LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection
by: Abassy, Mervat, et al.
Published: (2024)
by: Abassy, Mervat, et al.
Published: (2024)
Are AI-Generated Text Detectors Robust to Adversarial Perturbations?
by: Huang, Guanhua, et al.
Published: (2024)
by: Huang, Guanhua, et al.
Published: (2024)
Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification
by: Wang, Zihan, et al.
Published: (2024)
by: Wang, Zihan, et al.
Published: (2024)
Real, Fake, or Manipulated? Detecting Machine-Influenced Text
by: Wang, Yitong, et al.
Published: (2025)
by: Wang, Yitong, et al.
Published: (2025)
Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks
by: Zhang, Xinyu, et al.
Published: (2023)
by: Zhang, Xinyu, et al.
Published: (2023)
Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy
by: Zhang, Shuhai, et al.
Published: (2024)
by: Zhang, Shuhai, et al.
Published: (2024)
Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks
by: Yue, Shengbin, et al.
Published: (2024)
by: Yue, Shengbin, et al.
Published: (2024)
Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation
by: Xue, Yanni, et al.
Published: (2024)
by: Xue, Yanni, et al.
Published: (2024)
RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection
by: Zhou, Ziyang, et al.
Published: (2026)
by: Zhou, Ziyang, et al.
Published: (2026)
Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation
by: Zhong, Tianqi, et al.
Published: (2024)
by: Zhong, Tianqi, et al.
Published: (2024)
Similar Items
-
T-Detect: Tail-Aware Statistical Normalization for Robust Detection of Adversarial Machine-Generated Text
by: West, Alva, et al.
Published: (2025) -
MAGE: Machine-generated Text Detection in the Wild
by: Li, Yafu, et al.
Published: (2023) -
LM$^2$otifs : An Explainable Framework for Machine-Generated Texts Detection
by: Zheng, Xu, et al.
Published: (2025) -
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection
by: Yang, Tiankai, et al.
Published: (2025) -
ACE-Sync: An Adaptive Cloud-Edge Synchronization Framework for Communication-Efficient Large-Scale Distributed Model Training
by: Yang, Yi, et al.
Published: (2025)