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Found 5 papers, 1 papers with code
Towards Stronger Adversarial Baselines Through Human-AI Collaboration
We propose to combine human and AI expertise in generating adversarial examples, benefiting from humans’ expertise in language and automated attacks’ ability to probe the target system more quickly and thoroughly.
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations
Adversarial attacks curated against NLP models are increasingly becoming practical threats.
Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers
Backdoor attacks manipulate model predictions by inserting innocuous triggers into training and test data.
TCAB: A Large-Scale Text Classification Attack Benchmark
In addition to the primary tasks of detecting and labeling attacks, TCAB can also be used for attack localization, attack target labeling, and attack characterization.
Identifying Adversarial Attacks on Text Classifiers
The landscape of adversarial attacks against text classifiers continues to grow, with new attacks developed every year and many of them available in standard toolkits, such as TextAttack and OpenAttack.
