# clean-label
4 articlestagged with “clean-label”
Clean-Label Poisoning Attacks
Creating poisoned training samples that maintain correct labels while still influencing model behavior through subtle feature manipulation.
Clean-Label Data Poisoning
Deep dive into clean-label poisoning attacks that corrupt model behavior without modifying labels, including gradient-based methods, feature collision, and witches' brew attacks.
Data Poisoning Methods
Practical methodology for poisoning training datasets at scale, including crowdsource manipulation, web-scale dataset attacks, label flipping, feature collision, bilevel optimization for poison selection, and detection evasion techniques.
Poisoning Fine-Tuning Datasets
Techniques for inserting backdoor triggers into fine-tuning datasets, clean-label poisoning that evades content filters, and scaling attacks across dataset sizes -- how adversarial training data compromises model behavior.