Efficient Black-Box Adversarial Attacks on Neural Text Detectors

3 November 2023

Papers citing "Efficient Black-Box Adversarial Attacks on Neural Text Detectors"

9 / 9 papers shown

Title
Can AI-Generated Text be Reliably Detected? Vinu Sankar Sadasivan Aounon Kumar S. Balasubramanian Wenxiao Wang Soheil Feizi DeLMO 238 389 0 20 Jan 2025
TextDefense: Adversarial Text Detection based on Word Importance Entropy Lujia Shen Xuhong Zhang S. Ji Yuwen Pu Chunpeng Ge Xing Yang Yanghe Feng AAML 44 8 0 12 Feb 2023
Mutation-Based Adversarial Attacks on Neural Text Detectors G. Liang Jesus Guerrero I. Alsmadi DeLMO 61 9 0 11 Feb 2023
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series Pradeep Rathore Arghya Basak S. Nistala Venkataramana Runkana AAML 69 42 0 13 Jan 2021
Automatic Detection of Machine Generated Text: A Critical Survey Ganesh Jawahar Muhammad Abdul-Mageed L. Lakshmanan DeLMO 76 236 0 02 Nov 2020
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment Di Jin Zhijing Jin Qiufeng Wang Peter Szolovits SILM AAML 191 1,088 0 27 Jul 2019
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers Ji Gao Jack Lanchantin M. Soffa Yanjun Qi AAML 140 725 0 13 Jan 2018
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,129 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 284 14,968 1 21 Dec 2013