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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 226–250 of 492 papers

TitleStatusHype
Poisoning Programs by Un-Repairing Code: Security Concerns of AI-generated Code—0
Policy Teaching via Data Poisoning in Learning from Human Preferences—0
Post-Training Overfitting Mitigation in DNN Classifiers—0
Practical Data Poisoning Attack against Next-Item Recommendation—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
Practical Poisoning Attacks on Neural Networks—0
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning—0
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning—0
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release—0
PrivacyGAN: robust generative image privacy—0
Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models—0
Property Inference From Poisoning—0
Protecting against simultaneous data poisoning attacks—0
Protecting Proprietary Data: Poisoning for Secure Dataset Release—0
Provably effective detection of effective data poisoning attacks—0
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning—0
Proving Data-Poisoning Robustness in Decision Trees—0
Purifying Large Language Models by Ensembling a Small Language Model—0
QTrojan: A Circuit Backdoor Against Quantum Neural Networks—0
Reaching Data Confidentiality and Model Accountability on the CalTrain—0
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning—0
Redactor: A Data-centric and Individualized Defense Against Inference Attacks—0
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning—0
Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation—0
Regularization Helps with Mitigating Poisoning Attacks: Distributionally-Robust Machine Learning Using the Wasserstein Distance—0
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