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Data Poisoning

Data Poisoning is an adversarial attack that tries to manipulate the training dataset in order to control the prediction behavior of a trained model such that the model will label malicious examples into a desired classes (e.g., labeling spam e-mails as safe).

Source: Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Papers

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Showing 151–160 of 492 papers

TitleStatusHype
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Purifying Large Language Models by Ensembling a Small Language Model—0
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based AgentsCode2
SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms—0
Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems—0
The Effect of Data Poisoning on Counterfactual ExplanationsCode0
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language ModelsCode2
Game-Theoretic Unlearnable Example GeneratorCode0
Security and Privacy Challenges of Large Language Models: A Survey—0
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks—0
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